Setup

Load Packages

##Install Packages if Needed
if (!require("ggplot2")) install.packages("ggplot2")
Loading required package: ggplot2
if (!require("effectsize")) install.packages("effectsize")
Loading required package: effectsize
if (!require("emmeans")) install.packages("emmeans")
Loading required package: emmeans
if (!require("dplyr")) install.packages("dplyr")
Loading required package: dplyr

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union
if (!require("tidyr")) install.packages("tidyr")
Loading required package: tidyr
if (!require("Rmisc")) install.packages("Rmisc")
Loading required package: Rmisc
Loading required package: lattice
Loading required package: plyr
----------------------------------------------------------------------------------
You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)
----------------------------------------------------------------------------------

Attaching package: ‘plyr’

The following objects are masked from ‘package:dplyr’:

    arrange, count, desc, failwith, id, mutate, rename, summarise,
    summarize
if (!require("ggpubr")) install.packages("ggpubr")
Loading required package: ggpubr

Attaching package: ‘ggpubr’

The following object is masked from ‘package:plyr’:

    mutate
if (!require("cowplot")) install.packages("cowplot")
Loading required package: cowplot

Attaching package: ‘cowplot’

The following object is masked from ‘package:ggpubr’:

    get_legend
if (!require("gridGraphics")) install.packages("gridGraphics")
Loading required package: gridGraphics
Warning: package ‘gridGraphics’ was built under R version 4.3.2Loading required package: grid
if (!require("tidyr")) install.packages("tidyr")

##Load Packages
library(ggplot2) #Required for plotting
library(effectsize) #Required for eta_squared effect sizes
library(emmeans) #Required for pairwise comparisons
library(dplyr) #Required to seperate columns in dataframe
library(tidyr) #Required for data organization
library(Rmisc) #Required for summarySE for summary statistics
library(ggpubr) #Required for adding pairwise p-values to plots with stat_pvalue_manual 
library(cowplot) #Required for arranging ggplots 
library(gridGraphics) #Required for adding labels to arranged plots
library(tidyr) #Required for reshaping datafrom from a wide to long format.

Note: Run “Graphing Parameters” section from 01_ExperimentalSetup.R file

Load and Organize Data

Note: Full Data with Bleaching Metrics and Color Scores created in 04_Models.R file

#Load Data
FullData<-read.csv("Outputs/FullData.csv", header=TRUE)


#Set factor variables 
FullData$TimeP<-factor(FullData$TimeP, levels=c("W1", "W2", "M1", "M4", "M8", "M12"), ordered=TRUE)
FullData$Site<-factor(FullData$Site, levels=c("KL", "SS"), ordered=TRUE)
FullData$Genotype<-factor(FullData$Genotype, levels=c("AC10", "AC12", "AC8"), ordered=TRUE)
FullData$Treatment<-factor(FullData$Treatment, levels=c("Control", "Heat"), ordered=TRUE)
FullData$Treat<-factor(FullData$Treat, levels=c("C", "H"), ordered=TRUE)

Percent Retention

Subset Thermal Assay Data by Treatment

##Control
FullData_C<-subset(FullData, Treat=="C")

##Heated
FullData_H<-subset(FullData, Treat=="H")

Calculate Percent Retention

Calculating retention as proportion remaining relative to corresponding control levels (0-1) for each site and genotype at each timepoint.

##Calculate averages for Control Treatment for each Site, Genotype, and Timepoint 
names(FullData_C)
 [1] "ID"          "RandN"       "TimeP"       "Site"       
 [5] "Genotype"    "Treat"       "Treatment"   "Set"        
 [9] "Score_Full"  "Score_TP"    "Score_Set"   "Score_Seas" 
[13] "AnSet"       "Season"      "SA_cm2"      "Chl_ug.cm2" 
[17] "Sym10.6_cm2"
FullData_C.a<-aggregate(FullData_C[,c(9:10, 16:17)], list(FullData_C$Site, FullData_C$Genotype, FullData_C$TimeP), mean, na.action = na.omit)
names(FullData_C.a)[1:3]<-c("Site", "Genotype", "TimeP")
names(FullData_C.a)[4:7]<-paste(names(FullData_C)[c(9:10, 16:17)], "C", sep="_")

##Merge Control Averages with Heated Samples
#Merges by Site, Genotype, and Timepoint
TolData<-merge(FullData_H, FullData_C.a, all.x=TRUE )

##Calculate Proportion Retained for each Bleaching Metric relative to Control
TolData$Score_Full.prop<-round((TolData$Score_Full/TolData$Score_Full_C), 4)
TolData$Score_TP.prop<-round((TolData$Score_TP/TolData$Score_TP_C), 4)
TolData$Chl.prop<-round((TolData$Chl_ug.cm2/TolData$Chl_ug.cm2_C), 4)
TolData$Sym.prop<-round((TolData$Sym10.6_cm2/TolData$Sym10.6_cm2_C), 4)

##Set values >1 to 1
TolData$Score_Full.prop[which(TolData$Score_Full.prop>1)]<-1.0000
TolData$Score_TP.prop[which(TolData$Score_TP.prop>1)]<-1.0000
TolData$Chl.prop[which(TolData$Chl.prop>1)]<-1.0000
TolData$Sym.prop[which(TolData$Sym.prop>1)]<-1.0000

##Create Site and Genotype Variable
TolData$Site.Geno<-paste(TolData$Site, TolData$Genotype, sep="_")

Subset by Timepoint

TolData_W1<-subset(TolData, TimeP=="W1")
TolData_W2<-subset(TolData, TimeP=="W2")
TolData_M1<-subset(TolData, TimeP=="M1")
TolData_M4<-subset(TolData, TimeP=="M4")
TolData_M8<-subset(TolData, TimeP=="M8")
TolData_M12<-subset(TolData, TimeP=="M12")

Thermal Tolerance Chlorophyll

Full Set

Run Model

##Check normality
hist(TolData$Chl.prop)

shapiro.test(TolData$Chl.prop)

    Shapiro-Wilk normality test

data:  TolData$Chl.prop
W = 0.87577, p-value = 2.433e-09
#Not Normal

##Try log transformation
hist(log(TolData$Chl.prop))

shapiro.test(log(TolData$Chl.prop))

    Shapiro-Wilk normality test

data:  log(TolData$Chl.prop)
W = 0.97275, p-value = 0.007572
#Not normal but improved

##Try square root transformation
hist(sqrt(TolData$Chl.prop))

shapiro.test(log(TolData$Chl.prop))

    Shapiro-Wilk normality test

data:  log(TolData$Chl.prop)
W = 0.97275, p-value = 0.007572
#Not normal but much improved


##Model as a function of Site and Genotype and Timepoint
##Model with sqrt transformation
Tol_Chl_lm<-lm(sqrt(Chl.prop)~Site+Genotype+TimeP+ Site:Genotype + Site:TimeP + Genotype:TimeP, data=TolData)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Chl_lm)))


#Q-Q plot
qqnorm(resid(Tol_Chl_lm)); qqline(resid(Tol_Chl_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Chl_lm), resid(Tol_Chl_lm))

Model Results

#Model Results
summary(Tol_Chl_lm)

Call:
lm(formula = sqrt(Chl.prop) ~ Site + Genotype + TimeP + Site:Genotype + 
    Site:TimeP + Genotype:TimeP, data = TolData)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.161251 -0.046726  0.002497  0.047694  0.155492 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         0.452966   0.006329  71.574  < 2e-16 ***
Site.L             -0.015067   0.008943  -1.685 0.094840 .  
Genotype.L         -0.062429   0.010774  -5.794 6.51e-08 ***
Genotype.Q         -0.028845   0.011149  -2.587 0.010963 *  
TimeP.L             0.074391   0.015473   4.808 4.83e-06 ***
TimeP.Q            -0.163442   0.015445 -10.582  < 2e-16 ***
TimeP.C            -0.246251   0.015541 -15.845  < 2e-16 ***
TimeP^4            -0.194710   0.015559 -12.515  < 2e-16 ***
TimeP^5            -0.031239   0.015492  -2.017 0.046157 *  
Site.L:Genotype.L   0.042483   0.015237   2.788 0.006237 ** 
Site.L:Genotype.Q   0.076176   0.015753   4.836 4.30e-06 ***
Site.L:TimeP.L     -0.009791   0.021881  -0.447 0.655430    
Site.L:TimeP.Q     -0.044698   0.021792  -2.051 0.042608 *  
Site.L:TimeP.C      0.038173   0.021968   1.738 0.085041 .  
Site.L:TimeP^4     -0.052140   0.021983  -2.372 0.019422 *  
Site.L:TimeP^5     -0.038620   0.021831  -1.769 0.079630 .  
Genotype.L:TimeP.L  0.181790   0.026311   6.909 3.22e-10 ***
Genotype.Q:TimeP.L  0.085212   0.027251   3.127 0.002256 ** 
Genotype.L:TimeP.Q  0.046286   0.026082   1.775 0.078706 .  
Genotype.Q:TimeP.Q  0.022110   0.027395   0.807 0.421335    
Genotype.L:TimeP.C -0.118908   0.026590  -4.472 1.88e-05 ***
Genotype.Q:TimeP.C  0.063014   0.027179   2.318 0.022256 *  
Genotype.L:TimeP^4 -0.106570   0.026696  -3.992 0.000118 ***
Genotype.Q:TimeP^4  0.004505   0.027155   0.166 0.868544    
Genotype.L:TimeP^5  0.039262   0.026254   1.496 0.137620    
Genotype.Q:TimeP^5 -0.138355   0.027376  -5.054 1.72e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.0737 on 111 degrees of freedom
  (3 observations deleted due to missingness)
Multiple R-squared:  0.8756,    Adjusted R-squared:  0.8476 
F-statistic: 31.26 on 25 and 111 DF,  p-value: < 2.2e-16
anova(Tol_Chl_lm)
Analysis of Variance Table

Response: sqrt(Chl.prop)
                Df  Sum Sq Mean Sq  F value    Pr(>F)    
Site             1 0.01081 0.01081   1.9894  0.161201    
Genotype         2 0.24811 0.12405  22.8362 4.936e-09 ***
TimeP            5 3.00659 0.60132 110.6917 < 2.2e-16 ***
Site:Genotype    2 0.17669 0.08834  16.2624 6.397e-07 ***
Site:TimeP       5 0.09374 0.01875   3.4513  0.006159 ** 
Genotype:TimeP  10 0.70955 0.07096  13.0616 7.640e-15 ***
Residuals      111 0.60299 0.00543                       
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Effect Size of Predictors
eta_squared(Tol_Chl_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter      |     Eta2 |       95% CI
----------------------------------------
Site           | 2.23e-03 | [0.00, 1.00]
Genotype       |     0.05 | [0.00, 1.00]
TimeP          |     0.62 | [0.52, 1.00]
Site:Genotype  |     0.04 | [0.00, 1.00]
Site:TimeP     |     0.02 | [0.00, 1.00]
Genotype:TimeP |     0.15 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_Chl_lm.res<-data.frame(anova(Tol_Chl_lm))
Tol_Chl_lm.res$Predictor<-rownames(Tol_Chl_lm.res)
Tol_Chl_lm.res$EtaSq<-c(eta_squared(Tol_Chl_lm, partial=FALSE)$Eta2, NA)
Tol_Chl_lm.res$Response<-rep("Chlorophyll", nrow(Tol_Chl_lm.res))
Tol_Chl_lm.res<-Tol_Chl_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

Strong influence of Timepoint, including interactions with main variables of interest, Site and Genotype. Will analyze each Timepoint individually.

Chl Timepoint W1

Run Model

##Check normality
hist(TolData_W1$Chl.prop)

shapiro.test(TolData_W1$Chl.prop)

    Shapiro-Wilk normality test

data:  TolData_W1$Chl.prop
W = 0.90425, p-value = 0.03095
#Not Normal

##Try log+1 transformation
hist(log(TolData_W1$Chl.prop+1))

shapiro.test(log(TolData_W1$Chl.prop+1))

    Shapiro-Wilk normality test

data:  log(TolData_W1$Chl.prop + 1)
W = 0.92076, p-value = 0.06917
#Normal

##Model as a function of Site and Genotype
##Model with log+1 transformation
Tol_Chl_W1_lm<-lm(log(Chl.prop+1)~Site+Genotype+Site:Genotype, data=TolData_W1)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Chl_W1_lm)))


#Q-Q plot
qqnorm(resid(Tol_Chl_W1_lm)); qqline(resid(Tol_Chl_W1_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Chl_W1_lm), resid(Tol_Chl_W1_lm))

Model Results

Overall

#Model Results
summary(Tol_Chl_W1_lm)

Call:
lm(formula = log(Chl.prop + 1) ~ Site + Genotype + Site:Genotype, 
    data = TolData_W1)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.082776 -0.019331 -0.009257  0.018133  0.101612 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.142259   0.009259  15.365 2.11e-11 ***
Site.L            -0.049071   0.013094  -3.748 0.001603 ** 
Genotype.L        -0.076006   0.015609  -4.869 0.000144 ***
Genotype.Q        -0.050863   0.016453  -3.091 0.006626 ** 
Site.L:Genotype.L  0.074725   0.022074   3.385 0.003519 ** 
Site.L:Genotype.Q  0.079154   0.023269   3.402 0.003395 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.04415 on 17 degrees of freedom
Multiple R-squared:  0.8104,    Adjusted R-squared:  0.7546 
F-statistic: 14.53 on 5 and 17 DF,  p-value: 1.259e-05
anova(Tol_Chl_W1_lm)
Analysis of Variance Table

Response: log(Chl.prop + 1)
              Df   Sum Sq  Mean Sq F value    Pr(>F)    
Site           1 0.027338 0.027338  14.025 0.0016121 ** 
Genotype       2 0.069398 0.034699  17.802 6.762e-05 ***
Site:Genotype  2 0.044890 0.022445  11.515 0.0006894 ***
Residuals     17 0.033135 0.001949                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Effect Size of Predictors
eta_squared(Tol_Chl_W1_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter     | Eta2 |       95% CI
-----------------------------------
Site          | 0.16 | [0.00, 1.00]
Genotype      | 0.40 | [0.07, 1.00]
Site:Genotype | 0.26 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_Chl_W1_lm.res<-data.frame(anova(Tol_Chl_W1_lm))
Tol_Chl_W1_lm.res$Predictor<-rownames(Tol_Chl_W1_lm.res)
Tol_Chl_W1_lm.res$EtaSq<-c(eta_squared(Tol_Chl_W1_lm, partial=FALSE)$Eta2, NA)
Tol_Chl_W1_lm.res$Response<-rep("Chlorophyll", nrow(Tol_Chl_W1_lm.res))
Tol_Chl_W1_lm.res$TimeP<-rep("W1", nrow(Tol_Chl_W1_lm.res))
Tol_Chl_W1_lm.res<-Tol_Chl_W1_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

Significant effect of Site and Genotype and Site*Genotype.

Pairwise

#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_Chl_W1_lm, pairwise~Genotype | Site)
$emmeans
Site = KL:
 Genotype emmean     SE df lower.CL upper.CL
 AC10     0.2244 0.0221 17  0.17788   0.2710
 AC12     0.2642 0.0221 17  0.21761   0.3108
 AC8      0.0422 0.0221 17 -0.00434   0.0888

Site = SS:
 Genotype emmean     SE df lower.CL upper.CL
 AC10     0.1260 0.0221 17  0.07945   0.1726
 AC12     0.1034 0.0255 17  0.04961   0.1572
 AC8      0.0933 0.0221 17  0.04669   0.1398

Results are given on the log(mu + 1) (not the response) scale. 
Confidence level used: 0.95 

$contrasts
Site = KL:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12  -0.0397 0.0312 17  -1.273  0.4289
 AC10 - AC8    0.1822 0.0312 17   5.837  0.0001
 AC12 - AC8    0.2219 0.0312 17   7.110  <.0001

Site = SS:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12   0.0226 0.0337 17   0.671  0.7830
 AC10 - AC8    0.0328 0.0312 17   1.049  0.5570
 AC12 - AC8    0.0101 0.0337 17   0.300  0.9516

Note: contrasts are still on the log(mu + 1) scale 
P value adjustment: tukey method for comparing a family of 3 estimates 
#Sites within Genotypes
emmeans(Tol_Chl_W1_lm, pairwise~Site | Genotype)
$emmeans
Genotype = AC10:
 Site emmean     SE df lower.CL upper.CL
 KL   0.2244 0.0221 17  0.17788   0.2710
 SS   0.1260 0.0221 17  0.07945   0.1726

Genotype = AC12:
 Site emmean     SE df lower.CL upper.CL
 KL   0.2642 0.0221 17  0.21761   0.3108
 SS   0.1034 0.0255 17  0.04961   0.1572

Genotype = AC8:
 Site emmean     SE df lower.CL upper.CL
 KL   0.0422 0.0221 17 -0.00434   0.0888
 SS   0.0933 0.0221 17  0.04669   0.1398

Results are given on the log(mu + 1) (not the response) scale. 
Confidence level used: 0.95 

$contrasts
Genotype = AC10:
 contrast estimate     SE df t.ratio p.value
 KL - SS    0.0984 0.0312 17   3.153  0.0058

Genotype = AC12:
 contrast estimate     SE df t.ratio p.value
 KL - SS    0.1608 0.0337 17   4.769  0.0002

Genotype = AC8:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.0510 0.0312 17  -1.635  0.1205

Note: contrasts are still on the log(mu + 1) scale 
##Save p-values

#Genotypes within Sites
Tol_Chl_W1_lm.geno<-data.frame(emmeans(Tol_Chl_W1_lm, pairwise~Genotype | Site)$contrasts)
Tol_Chl_W1_lm.geno<-Tol_Chl_W1_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_W1_lm.geno$group1<-paste(Tol_Chl_W1_lm.geno$Site, Tol_Chl_W1_lm.geno$group1, sep="_")
Tol_Chl_W1_lm.geno$group2<-paste(Tol_Chl_W1_lm.geno$Site, Tol_Chl_W1_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_Chl_W1_lm.site<-data.frame(emmeans(Tol_Chl_W1_lm, pairwise~Site | Genotype)$contrasts)
Tol_Chl_W1_lm.site<-Tol_Chl_W1_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_W1_lm.site$group1<-paste(Tol_Chl_W1_lm.site$group1, Tol_Chl_W1_lm.site$Genotype, sep="_")
Tol_Chl_W1_lm.site$group2<-paste(Tol_Chl_W1_lm.site$group2, Tol_Chl_W1_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_Chl_W1_lm.p<-rbind(Tol_Chl_W1_lm.geno[,c(1:2,4:8)], Tol_Chl_W1_lm.site[,c(1:2,4:8)])
Tol_Chl_W1_lm.p<-Tol_Chl_W1_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_Chl_W1_lm.p$Sig<-ifelse(Tol_Chl_W1_lm.p$p<0.001, "***", ifelse(Tol_Chl_W1_lm.p$p<0.01, "**", ifelse(Tol_Chl_W1_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_Chl_W1_lm.p$Response<-rep("Chlorophyll", nrow(Tol_Chl_W1_lm.p))
Tol_Chl_W1_lm.p$TimeP<-rep("W1", nrow(Tol_Chl_W1_lm.p))

Plot Retention by Site and Genotype

##Summary statistics by Site and Genotype
Tol_Chl_W1_SG<-summarySE(TolData_W1, measurevar="Chl.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_Chl_W1_SG.plot<-ggplot(Tol_Chl_W1_SG, aes(x=Site.Geno, y=Chl.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Chlorophyll Retention")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_Chl_W1_lm.p,  y.position=0.4, step.increase=0.25, label="Sig", hide.ns=TRUE); Tol_Chl_W1_SG.plot

Chl Timepoint W2

Run Model

##Check normality
hist(TolData_W2$Chl.prop)

shapiro.test(TolData_W2$Chl.prop)

    Shapiro-Wilk normality test

data:  TolData_W2$Chl.prop
W = 0.98407, p-value = 0.9671
#Normal

##Model as a function of Site and Genotype
Tol_Chl_W2_lm<-lm(Chl.prop~Site+Genotype+Site:Genotype, data=TolData_W2)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Chl_W2_lm)))


#Q-Q plot
qqnorm(resid(Tol_Chl_W2_lm)); qqline(resid(Tol_Chl_W2_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Chl_W2_lm), resid(Tol_Chl_W2_lm))

Model Results

Overall

#Model Results
summary(Tol_Chl_W2_lm)

Call:
lm(formula = Chl.prop ~ Site + Genotype + Site:Genotype, data = TolData_W2)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.089900 -0.031587 -0.008183  0.038881  0.103550 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.1842264  0.0119450  15.423 5.03e-11 ***
Site.L             0.0188110  0.0168928   1.114 0.281921    
Genotype.L        -0.0947759  0.0204291  -4.639 0.000273 ***
Genotype.Q        -0.0585870  0.0209464  -2.797 0.012921 *  
Site.L:Genotype.L -0.0002833  0.0288911  -0.010 0.992297    
Site.L:Genotype.Q  0.0366569  0.0296227   1.237 0.233769    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.05552 on 16 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.6758,    Adjusted R-squared:  0.5745 
F-statistic: 6.671 on 5 and 16 DF,  p-value: 0.001551
anova(Tol_Chl_W2_lm)
Analysis of Variance Table

Response: Chl.prop
              Df   Sum Sq  Mean Sq F value   Pr(>F)    
Site           1 0.005580 0.005580  1.8106 0.197195    
Genotype       2 0.092482 0.046241 15.0038 0.000214 ***
Site:Genotype  2 0.004732 0.002366  0.7677 0.480448    
Residuals     16 0.049311 0.003082                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Effect Size of Predictors
eta_squared(Tol_Chl_W2_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter     | Eta2 |       95% CI
-----------------------------------
Site          | 0.04 | [0.00, 1.00]
Genotype      | 0.61 | [0.29, 1.00]
Site:Genotype | 0.03 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_Chl_W2_lm.res<-data.frame(anova(Tol_Chl_W2_lm))
Tol_Chl_W2_lm.res$Predictor<-rownames(Tol_Chl_W2_lm.res)
Tol_Chl_W2_lm.res$EtaSq<-c(eta_squared(Tol_Chl_W2_lm, partial=FALSE)$Eta2, NA)
Tol_Chl_W2_lm.res$Response<-rep("Chlorophyll", nrow(Tol_Chl_W2_lm.res))
Tol_Chl_W2_lm.res$TimeP<-rep("W2", nrow(Tol_Chl_W2_lm.res))
Tol_Chl_W2_lm.res<-Tol_Chl_W2_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

Significant effect of Genotype. Still checking Site*Genotype for comparability across Timepoints.

Pairwise

#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_Chl_W2_lm, pairwise~Genotype | Site)
$emmeans
Site = KL:
 Genotype emmean     SE df lower.CL upper.CL
 AC10     0.2033 0.0278 16   0.1445    0.262
 AC12     0.2399 0.0278 16   0.1811    0.299
 AC8      0.0696 0.0278 16   0.0107    0.128

Site = SS:
 Genotype emmean     SE df lower.CL upper.CL
 AC10     0.2514 0.0278 16   0.1925    0.310
 AC12     0.2242 0.0321 16   0.1563    0.292
 AC8      0.1170 0.0321 16   0.0491    0.185

Confidence level used: 0.95 

$contrasts
Site = KL:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12  -0.0366 0.0393 16  -0.933  0.6280
 AC10 - AC8    0.1338 0.0393 16   3.407  0.0095
 AC12 - AC8    0.1704 0.0393 16   4.340  0.0014

Site = SS:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12   0.0272 0.0424 16   0.640  0.8003
 AC10 - AC8    0.1343 0.0424 16   3.168  0.0156
 AC12 - AC8    0.1072 0.0453 16   2.364  0.0753

P value adjustment: tukey method for comparing a family of 3 estimates 
#Sites within Genotypes
emmeans(Tol_Chl_W2_lm, pairwise~Site | Genotype)
$emmeans
Genotype = AC10:
 Site emmean     SE df lower.CL upper.CL
 KL   0.2033 0.0278 16   0.1445    0.262
 SS   0.2514 0.0278 16   0.1925    0.310

Genotype = AC12:
 Site emmean     SE df lower.CL upper.CL
 KL   0.2399 0.0278 16   0.1811    0.299
 SS   0.2242 0.0321 16   0.1563    0.292

Genotype = AC8:
 Site emmean     SE df lower.CL upper.CL
 KL   0.0696 0.0278 16   0.0107    0.128
 SS   0.1170 0.0321 16   0.0491    0.185

Confidence level used: 0.95 

$contrasts
Genotype = AC10:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.0481 0.0393 16  -1.224  0.2387

Genotype = AC12:
 contrast estimate     SE df t.ratio p.value
 KL - SS    0.0157 0.0424 16   0.371  0.7156

Genotype = AC8:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.0475 0.0424 16  -1.120  0.2793
##Save p-values

#Genotypes within Sites
Tol_Chl_W2_lm.geno<-data.frame(emmeans(Tol_Chl_W2_lm, pairwise~Genotype | Site)$contrasts)
Tol_Chl_W2_lm.geno<-Tol_Chl_W2_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_W2_lm.geno$group1<-paste(Tol_Chl_W2_lm.geno$Site, Tol_Chl_W2_lm.geno$group1, sep="_")
Tol_Chl_W2_lm.geno$group2<-paste(Tol_Chl_W2_lm.geno$Site, Tol_Chl_W2_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_Chl_W2_lm.site<-data.frame(emmeans(Tol_Chl_W2_lm, pairwise~Site | Genotype)$contrasts)
Tol_Chl_W2_lm.site<-Tol_Chl_W2_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_W2_lm.site$group1<-paste(Tol_Chl_W2_lm.site$group1, Tol_Chl_W2_lm.site$Genotype, sep="_")
Tol_Chl_W2_lm.site$group2<-paste(Tol_Chl_W2_lm.site$group2, Tol_Chl_W2_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_Chl_W2_lm.p<-rbind(Tol_Chl_W2_lm.geno[,c(1:2,4:8)], Tol_Chl_W2_lm.site[,c(1:2,4:8)])
Tol_Chl_W2_lm.p<-Tol_Chl_W2_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_Chl_W2_lm.p$Sig<-ifelse(Tol_Chl_W2_lm.p$p<0.001, "***", ifelse(Tol_Chl_W2_lm.p$p<0.01, "**", ifelse(Tol_Chl_W2_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_Chl_W2_lm.p$Response<-rep("Chlorophyll", nrow(Tol_Chl_W2_lm.p))
Tol_Chl_W2_lm.p$TimeP<-rep("W2", nrow(Tol_Chl_W2_lm.p))

Plot Retention by Site and Genotype

##Summary statistics by Site and Genotype
Tol_Chl_W2_SG<-summarySE(TolData_W2, measurevar="Chl.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_Chl_W2_SG.plot<-ggplot(Tol_Chl_W2_SG, aes(x=Site.Geno, y=Chl.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Chlorophyll Retention")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_Chl_W2_lm.p,  y.position=0.4, step.increase=0.25, label="Sig", hide.ns=TRUE); Tol_Chl_W2_SG.plot

Chl Timepoint M1

Run Model

##Check normality
hist(TolData_M1$Chl.prop)

shapiro.test(TolData_M1$Chl.prop)

    Shapiro-Wilk normality test

data:  TolData_M1$Chl.prop
W = 0.9306, p-value = 0.1263
#Normal

##Model as a function of Site and Genotype
Tol_Chl_M1_lm<-lm(Chl.prop~Site+Genotype+Site:Genotype, data=TolData_M1)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Chl_M1_lm)))


#Q-Q plot
qqnorm(resid(Tol_Chl_M1_lm)); qqline(resid(Tol_Chl_M1_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Chl_M1_lm), resid(Tol_Chl_M1_lm))

Model Results

Overall

#Model Results
summary(Tol_Chl_M1_lm)

Call:
lm(formula = Chl.prop ~ Site + Genotype + Site:Genotype, data = TolData_M1)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.107050 -0.034575  0.003479  0.025531  0.088050 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.17217    0.01178  14.619 1.12e-10 ***
Site.L             0.01036    0.01666   0.622   0.5428    
Genotype.L        -0.16337    0.01935  -8.442 2.74e-07 ***
Genotype.Q         0.06169    0.02139   2.883   0.0108 *  
Site.L:Genotype.L  0.03819    0.02737   1.395   0.1820    
Site.L:Genotype.Q  0.05454    0.03026   1.803   0.0903 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.05474 on 16 degrees of freedom
Multiple R-squared:  0.8424,    Adjusted R-squared:  0.7932 
F-statistic: 17.11 on 5 and 16 DF,  p-value: 6.411e-06
anova(Tol_Chl_M1_lm)
Analysis of Variance Table

Response: Chl.prop
              Df   Sum Sq  Mean Sq F value    Pr(>F)    
Site           1 0.002283 0.002283  0.7619    0.3956    
Genotype       2 0.238424 0.119212 39.7888 6.168e-07 ***
Site:Genotype  2 0.015569 0.007785  2.5982    0.1054    
Residuals     16 0.047938 0.002996                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Effect Size of Predictors
eta_squared(Tol_Chl_M1_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter     |     Eta2 |       95% CI
---------------------------------------
Site          | 7.50e-03 | [0.00, 1.00]
Genotype      |     0.78 | [0.58, 1.00]
Site:Genotype |     0.05 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_Chl_M1_lm.res<-data.frame(anova(Tol_Chl_M1_lm))
Tol_Chl_M1_lm.res$Predictor<-rownames(Tol_Chl_M1_lm.res)
Tol_Chl_M1_lm.res$EtaSq<-c(eta_squared(Tol_Chl_M1_lm, partial=FALSE)$Eta2, NA)
Tol_Chl_M1_lm.res$Response<-rep("Chlorophyll", nrow(Tol_Chl_M1_lm.res))
Tol_Chl_M1_lm.res$TimeP<-rep("M1", nrow(Tol_Chl_M1_lm.res))
Tol_Chl_M1_lm.res<-Tol_Chl_M1_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

Significant effect of Genotype. Still checking Site*Genotype for comparability across Timepoints.

Pairwise

#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_Chl_M1_lm, pairwise~Genotype | Site)
$emmeans
Site = KL:
 Genotype emmean     SE df lower.CL upper.CL
 AC10     0.3089 0.0274 16   0.2509   0.3669
 AC12     0.1460 0.0316 16   0.0790   0.2130
 AC8      0.0397 0.0274 16  -0.0183   0.0977

Site = SS:
 Genotype emmean     SE df lower.CL upper.CL
 AC10     0.3169 0.0274 16   0.2588   0.3749
 AC12     0.0976 0.0316 16   0.0306   0.1646
 AC8      0.1240 0.0274 16   0.0660   0.1820

Confidence level used: 0.95 

$contrasts
Site = KL:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12   0.1629 0.0418 16   3.897  0.0035
 AC10 - AC8    0.2692 0.0387 16   6.956  <.0001
 AC12 - AC8    0.1063 0.0418 16   2.543  0.0538

Site = SS:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12   0.2192 0.0418 16   5.244  0.0002
 AC10 - AC8    0.1928 0.0387 16   4.983  0.0004
 AC12 - AC8   -0.0264 0.0418 16  -0.631  0.8056

P value adjustment: tukey method for comparing a family of 3 estimates 
#Sites within Genotypes
emmeans(Tol_Chl_M1_lm, pairwise~Site | Genotype)
$emmeans
Genotype = AC10:
 Site emmean     SE df lower.CL upper.CL
 KL   0.3089 0.0274 16   0.2509   0.3669
 SS   0.3169 0.0274 16   0.2588   0.3749

Genotype = AC12:
 Site emmean     SE df lower.CL upper.CL
 KL   0.1460 0.0316 16   0.0790   0.2130
 SS   0.0976 0.0316 16   0.0306   0.1646

Genotype = AC8:
 Site emmean     SE df lower.CL upper.CL
 KL   0.0397 0.0274 16  -0.0183   0.0977
 SS   0.1240 0.0274 16   0.0660   0.1820

Confidence level used: 0.95 

$contrasts
Genotype = AC10:
 contrast estimate     SE df t.ratio p.value
 KL - SS  -0.00795 0.0387 16  -0.205  0.8398

Genotype = AC12:
 contrast estimate     SE df t.ratio p.value
 KL - SS   0.04833 0.0447 16   1.081  0.2955

Genotype = AC8:
 contrast estimate     SE df t.ratio p.value
 KL - SS  -0.08432 0.0387 16  -2.179  0.0447
##Save p-values

#Genotypes within Sites
Tol_Chl_M1_lm.geno<-data.frame(emmeans(Tol_Chl_M1_lm, pairwise~Genotype | Site)$contrasts)
Tol_Chl_M1_lm.geno<-Tol_Chl_M1_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_M1_lm.geno$group1<-paste(Tol_Chl_M1_lm.geno$Site, Tol_Chl_M1_lm.geno$group1, sep="_")
Tol_Chl_M1_lm.geno$group2<-paste(Tol_Chl_M1_lm.geno$Site, Tol_Chl_M1_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_Chl_M1_lm.site<-data.frame(emmeans(Tol_Chl_M1_lm, pairwise~Site | Genotype)$contrasts)
Tol_Chl_M1_lm.site<-Tol_Chl_M1_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_M1_lm.site$group1<-paste(Tol_Chl_M1_lm.site$group1, Tol_Chl_M1_lm.site$Genotype, sep="_")
Tol_Chl_M1_lm.site$group2<-paste(Tol_Chl_M1_lm.site$group2, Tol_Chl_M1_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_Chl_M1_lm.p<-rbind(Tol_Chl_M1_lm.geno[,c(1:2,4:8)], Tol_Chl_M1_lm.site[,c(1:2,4:8)])
Tol_Chl_M1_lm.p<-Tol_Chl_M1_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_Chl_M1_lm.p$Sig<-ifelse(Tol_Chl_M1_lm.p$p<0.001, "***", ifelse(Tol_Chl_M1_lm.p$p<0.01, "**", ifelse(Tol_Chl_M1_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_Chl_M1_lm.p$Response<-rep("Chlorophyll", nrow(Tol_Chl_M1_lm.p))
Tol_Chl_M1_lm.p$TimeP<-rep("M1", nrow(Tol_Chl_M1_lm.p))

Plot Retention by Site and Genotype

##Summary statistics by Site and Genotype
Tol_Chl_M1_SG<-summarySE(TolData_M1, measurevar="Chl.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_Chl_M1_SG.plot<-ggplot(Tol_Chl_M1_SG, aes(x=Site.Geno, y=Chl.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Chlorophyll Retention")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_Chl_M1_lm.p,  y.position=0.4, step.increase=0.25, label="Sig", hide.ns=TRUE); Tol_Chl_M1_SG.plot

Chl Timepoint M4

Run Model

##Check normality
hist(TolData_M4$Chl.prop)

shapiro.test(TolData_M4$Chl.prop)

    Shapiro-Wilk normality test

data:  TolData_M4$Chl.prop
W = 0.91299, p-value = 0.04096
#Not Normal

##Try log+1 transformation
hist(log(TolData_M4$Chl.prop+1))

shapiro.test(log(TolData_M4$Chl.prop+1))

    Shapiro-Wilk normality test

data:  log(TolData_M4$Chl.prop + 1)
W = 0.93102, p-value = 0.1028
#Normal

##Model as a function of Site and Genotype
##Model with log transformation
Tol_Chl_M4_lm<-lm(log(Chl.prop+1)~Site+Genotype+Site:Genotype, data=TolData_M4)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Chl_M4_lm)))


#Q-Q plot
qqnorm(resid(Tol_Chl_M4_lm)); qqline(resid(Tol_Chl_M4_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Chl_M4_lm), resid(Tol_Chl_M4_lm))

Model Results

Overall

#Model Results
summary(Tol_Chl_M4_lm)

Call:
lm(formula = log(Chl.prop + 1) ~ Site + Genotype + Site:Genotype, 
    data = TolData_M4)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.062753 -0.025900 -0.004896  0.017009  0.065759 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.23863    0.00807  29.568  < 2e-16 ***
Site.L            -0.04272    0.01141  -3.743  0.00149 ** 
Genotype.L        -0.03027    0.01398  -2.166  0.04400 *  
Genotype.Q        -0.11052    0.01398  -7.907  2.9e-07 ***
Site.L:Genotype.L -0.03189    0.01977  -1.613  0.12415    
Site.L:Genotype.Q  0.05753    0.01977   2.910  0.00934 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.03954 on 18 degrees of freedom
Multiple R-squared:  0.8368,    Adjusted R-squared:  0.7915 
F-statistic: 18.46 on 5 and 18 DF,  p-value: 1.598e-06
anova(Tol_Chl_M4_lm)
Analysis of Variance Table

Response: log(Chl.prop + 1)
              Df   Sum Sq  Mean Sq F value    Pr(>F)    
Site           1 0.021901 0.021901 14.0102  0.001489 ** 
Genotype       2 0.105056 0.052528 33.6031 8.379e-07 ***
Site:Genotype  2 0.017304 0.008652  5.5349  0.013381 *  
Residuals     18 0.028137 0.001563                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Effect Size of Predictors
eta_squared(Tol_Chl_M4_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter     | Eta2 |       95% CI
-----------------------------------
Site          | 0.13 | [0.00, 1.00]
Genotype      | 0.61 | [0.32, 1.00]
Site:Genotype | 0.10 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_Chl_M4_lm.res<-data.frame(anova(Tol_Chl_M4_lm))
Tol_Chl_M4_lm.res$Predictor<-rownames(Tol_Chl_M4_lm.res)
Tol_Chl_M4_lm.res$EtaSq<-c(eta_squared(Tol_Chl_M4_lm, partial=FALSE)$Eta2, NA)
Tol_Chl_M4_lm.res$Response<-rep("Chlorophyll", nrow(Tol_Chl_M4_lm.res))
Tol_Chl_M4_lm.res$TimeP<-rep("M4", nrow(Tol_Chl_M4_lm.res))
Tol_Chl_M4_lm.res<-Tol_Chl_M4_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

Significant effect of Site and Genotype and Site*Genotype.

Pairwise

#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_Chl_M4_lm, pairwise~Genotype | Site)
$emmeans
Site = KL:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.213 0.0198 18    0.171    0.254
 AC12      0.392 0.0198 18    0.351    0.434
 AC8       0.202 0.0198 18    0.160    0.243

Site = SS:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.217 0.0198 18    0.176    0.259
 AC12      0.265 0.0198 18    0.224    0.307
 AC8       0.143 0.0198 18    0.101    0.184

Results are given on the log(mu + 1) (not the response) scale. 
Confidence level used: 0.95 

$contrasts
Site = KL:
 contrast    estimate    SE df t.ratio p.value
 AC10 - AC12  -0.1797 0.028 18  -6.428  <.0001
 AC10 - AC8    0.0109 0.028 18   0.391  0.9196
 AC12 - AC8    0.1906 0.028 18   6.819  <.0001

Site = SS:
 contrast    estimate    SE df t.ratio p.value
 AC10 - AC12  -0.0482 0.028 18  -1.724  0.2237
 AC10 - AC8    0.0747 0.028 18   2.672  0.0393
 AC12 - AC8    0.1229 0.028 18   4.396  0.0010

Note: contrasts are still on the log(mu + 1) scale 
P value adjustment: tukey method for comparing a family of 3 estimates 
#Sites within Genotypes
emmeans(Tol_Chl_M4_lm, pairwise~Site | Genotype)
$emmeans
Genotype = AC10:
 Site emmean     SE df lower.CL upper.CL
 KL    0.213 0.0198 18    0.171    0.254
 SS    0.217 0.0198 18    0.176    0.259

Genotype = AC12:
 Site emmean     SE df lower.CL upper.CL
 KL    0.392 0.0198 18    0.351    0.434
 SS    0.265 0.0198 18    0.224    0.307

Genotype = AC8:
 Site emmean     SE df lower.CL upper.CL
 KL    0.202 0.0198 18    0.160    0.243
 SS    0.143 0.0198 18    0.101    0.184

Results are given on the log(mu + 1) (not the response) scale. 
Confidence level used: 0.95 

$contrasts
Genotype = AC10:
 contrast estimate    SE df t.ratio p.value
 KL - SS  -0.00468 0.028 18  -0.167  0.8688

Genotype = AC12:
 contrast estimate    SE df t.ratio p.value
 KL - SS   0.12684 0.028 18   4.537  0.0003

Genotype = AC8:
 contrast estimate    SE df t.ratio p.value
 KL - SS   0.05909 0.028 18   2.114  0.0488

Note: contrasts are still on the log(mu + 1) scale 
##Save p-values

#Genotypes within Sites
Tol_Chl_M4_lm.geno<-data.frame(emmeans(Tol_Chl_M4_lm, pairwise~Genotype | Site)$contrasts)
Tol_Chl_M4_lm.geno<-Tol_Chl_M4_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_M4_lm.geno$group1<-paste(Tol_Chl_M4_lm.geno$Site, Tol_Chl_M4_lm.geno$group1, sep="_")
Tol_Chl_M4_lm.geno$group2<-paste(Tol_Chl_M4_lm.geno$Site, Tol_Chl_M4_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_Chl_M4_lm.site<-data.frame(emmeans(Tol_Chl_M4_lm, pairwise~Site | Genotype)$contrasts)
Tol_Chl_M4_lm.site<-Tol_Chl_M4_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_M4_lm.site$group1<-paste(Tol_Chl_M4_lm.site$group1, Tol_Chl_M4_lm.site$Genotype, sep="_")
Tol_Chl_M4_lm.site$group2<-paste(Tol_Chl_M4_lm.site$group2, Tol_Chl_M4_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_Chl_M4_lm.p<-rbind(Tol_Chl_M4_lm.geno[,c(1:2,4:8)], Tol_Chl_M4_lm.site[,c(1:2,4:8)])
Tol_Chl_M4_lm.p<-Tol_Chl_M4_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_Chl_M4_lm.p$Sig<-ifelse(Tol_Chl_M4_lm.p$p<0.001, "***", ifelse(Tol_Chl_M4_lm.p$p<0.01, "**", ifelse(Tol_Chl_M4_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_Chl_M4_lm.p$Response<-rep("Chlorophyll", nrow(Tol_Chl_M4_lm.p))
Tol_Chl_M4_lm.p$TimeP<-rep("M4", nrow(Tol_Chl_M4_lm.p))

Plot Retention by Site and Genotype

##Summary statistics by Site and Genotype
Tol_Chl_M4_SG<-summarySE(TolData_M4, measurevar="Chl.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_Chl_M4_SG.plot<-ggplot(Tol_Chl_M4_SG, aes(x=Site.Geno, y=Chl.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Chlorophyll Retention")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_Chl_M4_lm.p,  y.position=0.55, step.increase=0.20, label="Sig", hide.ns=TRUE); Tol_Chl_M4_SG.plot

Chl Timepoint M8

Run Model

##Check normality
hist(TolData_M8$Chl.prop)

shapiro.test(TolData_M8$Chl.prop)

    Shapiro-Wilk normality test

data:  TolData_M8$Chl.prop
W = 0.91958, p-value = 0.06527
#Normal

##Model as a function of Site and Genotype
Tol_Chl_M8_lm<-lm(Chl.prop~Site+Genotype+Site:Genotype, data=TolData_M8)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Chl_M8_lm)))


#Q-Q plot
qqnorm(resid(Tol_Chl_M8_lm)); qqline(resid(Tol_Chl_M8_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Chl_M8_lm), resid(Tol_Chl_M8_lm))

Model Results

Overall

#Model Results
summary(Tol_Chl_M8_lm)

Call:
lm(formula = Chl.prop ~ Site + Genotype + Site:Genotype, data = TolData_M8)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.289450 -0.084387 -0.004075  0.108575  0.201950 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.56837    0.03013  18.864 7.76e-13 ***
Site.L             0.01907    0.04261   0.448  0.66009    
Genotype.L         0.16787    0.05287   3.175  0.00553 ** 
Genotype.Q         0.01544    0.05149   0.300  0.76793    
Site.L:Genotype.L  0.10868    0.07477   1.454  0.16428    
Site.L:Genotype.Q  0.12765    0.07282   1.753  0.09764 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1437 on 17 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.4847,    Adjusted R-squared:  0.3331 
F-statistic: 3.198 on 5 and 17 DF,  p-value: 0.03243
anova(Tol_Chl_M8_lm)
Analysis of Variance Table

Response: Chl.prop
              Df  Sum Sq  Mean Sq F value  Pr(>F)  
Site           1 0.00863 0.008631  0.4182 0.52649  
Genotype       2 0.20931 0.104656  5.0704 0.01875 *
Site:Genotype  2 0.11206 0.056028  2.7145 0.09483 .
Residuals     17 0.35089 0.020640                  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Effect Size of Predictors
eta_squared(Tol_Chl_M8_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter     | Eta2 |       95% CI
-----------------------------------
Site          | 0.01 | [0.00, 1.00]
Genotype      | 0.31 | [0.01, 1.00]
Site:Genotype | 0.16 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_Chl_M8_lm.res<-data.frame(anova(Tol_Chl_M8_lm))
Tol_Chl_M8_lm.res$Predictor<-rownames(Tol_Chl_M8_lm.res)
Tol_Chl_M8_lm.res$EtaSq<-c(eta_squared(Tol_Chl_M8_lm, partial=FALSE)$Eta2, NA)
Tol_Chl_M8_lm.res$Response<-rep("Chlorophyll", nrow(Tol_Chl_M8_lm.res))
Tol_Chl_M8_lm.res$TimeP<-rep("M8", nrow(Tol_Chl_M8_lm.res))
Tol_Chl_M8_lm.res<-Tol_Chl_M8_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

Significant effect of Genotype. Marginal effect (p<0.1) of Site * Genotype. Still checking Site*Genotype for comparability across Timepoints.

Pairwise

#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_Chl_M8_lm, pairwise~Genotype | Site)
$emmeans
Site = KL:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.460 0.0718 17    0.308    0.612
 AC12      0.616 0.0718 17    0.464    0.768
 AC8       0.589 0.0718 17    0.437    0.740

Site = SS:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.452 0.0829 17    0.277    0.627
 AC12      0.496 0.0718 17    0.344    0.647
 AC8       0.798 0.0718 17    0.646    0.950

Confidence level used: 0.95 

$contrasts
Site = KL:
 contrast    estimate    SE df t.ratio p.value
 AC10 - AC12  -0.1560 0.102 17  -1.536  0.2998
 AC10 - AC8   -0.1287 0.102 17  -1.267  0.4320
 AC12 - AC8    0.0273 0.102 17   0.268  0.9611

Site = SS:
 contrast    estimate    SE df t.ratio p.value
 AC10 - AC12  -0.0436 0.110 17  -0.397  0.9171
 AC10 - AC8   -0.3461 0.110 17  -3.154  0.0152
 AC12 - AC8   -0.3025 0.102 17  -2.978  0.0218

P value adjustment: tukey method for comparing a family of 3 estimates 
#Sites within Genotypes
emmeans(Tol_Chl_M8_lm, pairwise~Site | Genotype)
$emmeans
Genotype = AC10:
 Site emmean     SE df lower.CL upper.CL
 KL    0.460 0.0718 17    0.308    0.612
 SS    0.452 0.0829 17    0.277    0.627

Genotype = AC12:
 Site emmean     SE df lower.CL upper.CL
 KL    0.616 0.0718 17    0.464    0.768
 SS    0.496 0.0718 17    0.344    0.647

Genotype = AC8:
 Site emmean     SE df lower.CL upper.CL
 KL    0.589 0.0718 17    0.437    0.740
 SS    0.798 0.0718 17    0.646    0.950

Confidence level used: 0.95 

$contrasts
Genotype = AC10:
 contrast estimate    SE df t.ratio p.value
 KL - SS   0.00801 0.110 17   0.073  0.9427

Genotype = AC12:
 contrast estimate    SE df t.ratio p.value
 KL - SS   0.12043 0.102 17   1.185  0.2522

Genotype = AC8:
 contrast estimate    SE df t.ratio p.value
 KL - SS  -0.20935 0.102 17  -2.061  0.0550
##Save p-values

#Genotypes within Sites
Tol_Chl_M8_lm.geno<-data.frame(emmeans(Tol_Chl_M8_lm, pairwise~Genotype | Site)$contrasts)
Tol_Chl_M8_lm.geno<-Tol_Chl_M8_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_M8_lm.geno$group1<-paste(Tol_Chl_M8_lm.geno$Site, Tol_Chl_M8_lm.geno$group1, sep="_")
Tol_Chl_M8_lm.geno$group2<-paste(Tol_Chl_M8_lm.geno$Site, Tol_Chl_M8_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_Chl_M8_lm.site<-data.frame(emmeans(Tol_Chl_M8_lm, pairwise~Site | Genotype)$contrasts)
Tol_Chl_M8_lm.site<-Tol_Chl_M8_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_M8_lm.site$group1<-paste(Tol_Chl_M8_lm.site$group1, Tol_Chl_M8_lm.site$Genotype, sep="_")
Tol_Chl_M8_lm.site$group2<-paste(Tol_Chl_M8_lm.site$group2, Tol_Chl_M8_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_Chl_M8_lm.p<-rbind(Tol_Chl_M8_lm.geno[,c(1:2,4:8)], Tol_Chl_M8_lm.site[,c(1:2,4:8)])
Tol_Chl_M8_lm.p<-Tol_Chl_M8_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_Chl_M8_lm.p$Sig<-ifelse(Tol_Chl_M8_lm.p$p<0.001, "***", ifelse(Tol_Chl_M8_lm.p$p<0.01, "**", ifelse(Tol_Chl_M8_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_Chl_M8_lm.p$Response<-rep("Chlorophyll", nrow(Tol_Chl_M8_lm.p))
Tol_Chl_M8_lm.p$TimeP<-rep("M8", nrow(Tol_Chl_M8_lm.p))

Plot Retention by Site and Genotype

##Summary statistics by Site and Genotype
Tol_Chl_M8_SG<-summarySE(TolData_M8, measurevar="Chl.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_Chl_M8_SG.plot<-ggplot(Tol_Chl_M8_SG, aes(x=Site.Geno, y=Chl.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Chlorophyll Retention")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_Chl_M8_lm.p,  y.position=0.65, step.increase=0.6, label="Sig", hide.ns=TRUE); Tol_Chl_M8_SG.plot

Chl Timepoint M12

Run Model

##Check normality
hist(TolData_M12$Chl.prop)

shapiro.test(TolData_M12$Chl.prop)

    Shapiro-Wilk normality test

data:  TolData_M12$Chl.prop
W = 0.91232, p-value = 0.04572
#Not Normal

##Try log+1 transformation
hist(log(TolData_M12$Chl.prop+1))

shapiro.test(log(TolData_M12$Chl.prop+1))

    Shapiro-Wilk normality test

data:  log(TolData_M12$Chl.prop + 1)
W = 0.92396, p-value = 0.08104
#Normal

##Model as a function of Site and Genotype
##Model with log transformation
Tol_Chl_M12_lm<-lm(log(Chl.prop+1)~Site+Genotype+Site:Genotype, data=TolData_M12)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Chl_M12_lm)))


#Q-Q plot
qqnorm(resid(Tol_Chl_M12_lm)); qqline(resid(Tol_Chl_M12_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Chl_M12_lm), resid(Tol_Chl_M12_lm))

Model Results

Overall

#Model Results
summary(Tol_Chl_M12_lm)

Call:
lm(formula = log(Chl.prop + 1) ~ Site + Genotype + Site:Genotype, 
    data = TolData_M12)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.058954 -0.020387 -0.005506  0.017762  0.103832 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.079668   0.009314   8.553 1.45e-07 ***
Site.L            -0.020944   0.013173  -1.590    0.130    
Genotype.L         0.005873   0.015703   0.374    0.713    
Genotype.Q         0.020903   0.016552   1.263    0.224    
Site.L:Genotype.L  0.013279   0.022207   0.598    0.558    
Site.L:Genotype.Q  0.028084   0.023408   1.200    0.247    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.04441 on 17 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.2381,    Adjusted R-squared:  0.01406 
F-statistic: 1.063 on 5 and 17 DF,  p-value: 0.4147
anova(Tol_Chl_M12_lm)
Analysis of Variance Table

Response: log(Chl.prop + 1)
              Df   Sum Sq   Mean Sq F value Pr(>F)
Site           1 0.004059 0.0040587  2.0575 0.1696
Genotype       2 0.002879 0.0014393  0.7296 0.4966
Site:Genotype  2 0.003545 0.0017723  0.8985 0.4257
Residuals     17 0.033534 0.0019726               
#Effect Size of Predictors
eta_squared(Tol_Chl_M12_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter     | Eta2 |       95% CI
-----------------------------------
Site          | 0.09 | [0.00, 1.00]
Genotype      | 0.07 | [0.00, 1.00]
Site:Genotype | 0.08 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_Chl_M12_lm.res<-data.frame(anova(Tol_Chl_M12_lm))
Tol_Chl_M12_lm.res$Predictor<-rownames(Tol_Chl_M12_lm.res)
Tol_Chl_M12_lm.res$EtaSq<-c(eta_squared(Tol_Chl_M12_lm, partial=FALSE)$Eta2, NA)
Tol_Chl_M12_lm.res$Response<-rep("Chlorophyll", nrow(Tol_Chl_M12_lm.res))
Tol_Chl_M12_lm.res$TimeP<-rep("M12", nrow(Tol_Chl_M12_lm.res))
Tol_Chl_M12_lm.res<-Tol_Chl_M12_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

No significant effects. Still checking Site*Genotype for comparability across Timepoints.

Pairwise

#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_Chl_M12_lm, pairwise~Genotype | Site)
$emmeans
Site = KL:
 Genotype emmean     SE df lower.CL upper.CL
 AC10     0.0974 0.0222 17   0.0505   0.1442
 AC12     0.0936 0.0222 17   0.0468   0.1405
 AC8      0.0924 0.0222 17   0.0456   0.1393

Site = SS:
 Genotype emmean     SE df lower.CL upper.CL
 AC10     0.0707 0.0222 17   0.0239   0.1176
 AC12     0.0316 0.0256 17  -0.0225   0.0857
 AC8      0.0923 0.0222 17   0.0454   0.1391

Results are given on the log(mu + 1) (not the response) scale. 
Confidence level used: 0.95 

$contrasts
Site = KL:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12  0.00377 0.0314 17   0.120  0.9921
 AC10 - AC8   0.00497 0.0314 17   0.158  0.9863
 AC12 - AC8   0.00121 0.0314 17   0.038  0.9992

Site = SS:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12  0.03913 0.0339 17   1.154  0.4957
 AC10 - AC8  -0.02158 0.0314 17  -0.687  0.7740
 AC12 - AC8  -0.06071 0.0339 17  -1.790  0.2027

Note: contrasts are still on the log(mu + 1) scale 
P value adjustment: tukey method for comparing a family of 3 estimates 
#Sites within Genotypes
emmeans(Tol_Chl_M12_lm, pairwise~Site | Genotype)
$emmeans
Genotype = AC10:
 Site emmean     SE df lower.CL upper.CL
 KL   0.0974 0.0222 17   0.0505   0.1442
 SS   0.0707 0.0222 17   0.0239   0.1176

Genotype = AC12:
 Site emmean     SE df lower.CL upper.CL
 KL   0.0936 0.0222 17   0.0468   0.1405
 SS   0.0316 0.0256 17  -0.0225   0.0857

Genotype = AC8:
 Site emmean     SE df lower.CL upper.CL
 KL   0.0924 0.0222 17   0.0456   0.1393
 SS   0.0923 0.0222 17   0.0454   0.1391

Results are given on the log(mu + 1) (not the response) scale. 
Confidence level used: 0.95 

$contrasts
Genotype = AC10:
 contrast estimate     SE df t.ratio p.value
 KL - SS  0.026684 0.0314 17   0.850  0.4073

Genotype = AC12:
 contrast estimate     SE df t.ratio p.value
 KL - SS  0.062047 0.0339 17   1.829  0.0850

Genotype = AC8:
 contrast estimate     SE df t.ratio p.value
 KL - SS  0.000126 0.0314 17   0.004  0.9969

Note: contrasts are still on the log(mu + 1) scale 
##Save p-values

#Genotypes within Sites
Tol_Chl_M12_lm.geno<-data.frame(emmeans(Tol_Chl_M12_lm, pairwise~Genotype | Site)$contrasts)
Tol_Chl_M12_lm.geno<-Tol_Chl_M12_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_M12_lm.geno$group1<-paste(Tol_Chl_M12_lm.geno$Site, Tol_Chl_M12_lm.geno$group1, sep="_")
Tol_Chl_M12_lm.geno$group2<-paste(Tol_Chl_M12_lm.geno$Site, Tol_Chl_M12_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_Chl_M12_lm.site<-data.frame(emmeans(Tol_Chl_M12_lm, pairwise~Site | Genotype)$contrasts)
Tol_Chl_M12_lm.site<-Tol_Chl_M12_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_M12_lm.site$group1<-paste(Tol_Chl_M12_lm.site$group1, Tol_Chl_M12_lm.site$Genotype, sep="_")
Tol_Chl_M12_lm.site$group2<-paste(Tol_Chl_M12_lm.site$group2, Tol_Chl_M12_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_Chl_M12_lm.p<-rbind(Tol_Chl_M12_lm.geno[,c(1:2,4:8)], Tol_Chl_M12_lm.site[,c(1:2,4:8)])
Tol_Chl_M12_lm.p<-Tol_Chl_M12_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_Chl_M12_lm.p$Sig<-ifelse(Tol_Chl_M12_lm.p$p<0.001, "***", ifelse(Tol_Chl_M12_lm.p$p<0.01, "**", ifelse(Tol_Chl_M12_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_Chl_M12_lm.p$Response<-rep("Chlorophyll", nrow(Tol_Chl_M12_lm.p))
Tol_Chl_M12_lm.p$TimeP<-rep("M12", nrow(Tol_Chl_M12_lm.p))

Plot Retention by Site and Genotype

##Summary statistics by Site and Genotype
Tol_Chl_M12_SG<-summarySE(TolData_M12, measurevar="Chl.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_Chl_M12_SG.plot<-ggplot(Tol_Chl_M12_SG, aes(x=Site.Geno, y=Chl.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Chlorophyll Retention")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o); Tol_Chl_M12_SG.plot


#+ stat_pvalue_manual(data=Tol_Chl_M12_lm.p,  y.position=0.2, step.increase=0.20, label="Sig", hide.ns=TRUE) #No significant differences

Thermal Tolerance Symbionts

Full Set

Run Model

##Check normality
hist(TolData$Sym.prop)

shapiro.test(TolData$Sym.prop)

    Shapiro-Wilk normality test

data:  TolData$Sym.prop
W = 0.91034, p-value = 9.046e-07
#Not Normal

##Try square transformation
hist((TolData$Sym.prop)^2)

shapiro.test((TolData$Sym.prop)^2)

    Shapiro-Wilk normality test

data:  (TolData$Sym.prop)^2
W = 0.86865, p-value = 9.119e-09
#Not normal 

##Try cubed transformation
hist((TolData$Sym.prop)^3)

shapiro.test((TolData$Sym.prop)^3)

    Shapiro-Wilk normality test

data:  (TolData$Sym.prop)^3
W = 0.81677, p-value = 9.284e-11
#Not normal 


##Model as a function of Site and Genotype and Timepoint
##Model with no transformation and check residuals
Tol_Sym_lm<-lm(Sym.prop~Site+Genotype+TimeP+ Site:Genotype + Site:TimeP + Genotype:TimeP, data=TolData)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Sym_lm)))


#Q-Q plot
qqnorm(resid(Tol_Sym_lm)); qqline(resid(Tol_Sym_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Sym_lm), resid(Tol_Sym_lm))

Model Results

#Model Results
summary(Tol_Sym_lm)

Call:
lm(formula = Sym.prop ~ Site + Genotype + TimeP + Site:Genotype + 
    Site:TimeP + Genotype:TimeP, data = TolData)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.39600 -0.09834 -0.01438  0.07946  0.42195 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         0.555690   0.013826  40.193  < 2e-16 ***
Site.L              0.009322   0.019517   0.478 0.634014    
Genotype.L         -0.226230   0.023747  -9.526 1.70e-15 ***
Genotype.Q          0.022268   0.024145   0.922 0.358711    
TimeP.L            -0.009548   0.030902  -0.309 0.758006    
TimeP.Q            -0.295353   0.030766  -9.600 1.18e-15 ***
TimeP.C            -0.389767   0.031170 -12.504  < 2e-16 ***
TimeP^4             0.004264   0.030821   0.138 0.890261    
Site.L:Genotype.L  -0.046952   0.033584  -1.398 0.165353    
Site.L:Genotype.Q   0.037992   0.034043   1.116 0.267229    
Site.L:TimeP.L     -0.063158   0.043658  -1.447 0.151286    
Site.L:TimeP.Q      0.079465   0.043491   1.827 0.070814 .  
Site.L:TimeP.C     -0.073283   0.043949  -1.667 0.098716 .  
Site.L:TimeP^4     -0.129699   0.043508  -2.981 0.003649 ** 
Genotype.L:TimeP.L  0.369878   0.053500   6.914 5.41e-10 ***
Genotype.Q:TimeP.L -0.039985   0.053547  -0.747 0.457073    
Genotype.L:TimeP.Q  0.004050   0.053272   0.076 0.939565    
Genotype.Q:TimeP.Q  0.044769   0.053306   0.840 0.403103    
Genotype.L:TimeP.C -0.191475   0.052900  -3.620 0.000476 ***
Genotype.Q:TimeP.C  0.152684   0.055056   2.773 0.006681 ** 
Genotype.L:TimeP^4 -0.080152   0.052728  -1.520 0.131804    
Genotype.Q:TimeP^4 -0.158952   0.054030  -2.942 0.004098 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1491 on 95 degrees of freedom
  (23 observations deleted due to missingness)
Multiple R-squared:  0.8235,    Adjusted R-squared:  0.7845 
F-statistic: 21.11 on 21 and 95 DF,  p-value: < 2.2e-16
anova(Tol_Sym_lm)
Analysis of Variance Table

Response: Sym.prop
               Df Sum Sq Mean Sq F value    Pr(>F)    
Site            1 0.0097 0.00970  0.4367  0.510299    
Genotype        2 1.9900 0.99498 44.7834 1.993e-14 ***
TimeP           4 5.5819 1.39547 62.8095 < 2.2e-16 ***
Site:Genotype   2 0.0709 0.03547  1.5965  0.208001    
Site:TimeP      4 0.4045 0.10111  4.5511  0.002088 ** 
Genotype:TimeP  8 1.7916 0.22394 10.0797 4.424e-10 ***
Residuals      95 2.1107 0.02222                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Effect Size of Predictors
eta_squared(Tol_Sym_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter      |     Eta2 |       95% CI
----------------------------------------
Site           | 8.11e-04 | [0.00, 1.00]
Genotype       |     0.17 | [0.06, 1.00]
TimeP          |     0.47 | [0.34, 1.00]
Site:Genotype  | 5.93e-03 | [0.00, 1.00]
Site:TimeP     |     0.03 | [0.00, 1.00]
Genotype:TimeP |     0.15 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_Sym_lm.res<-data.frame(anova(Tol_Sym_lm))
Tol_Sym_lm.res$Predictor<-rownames(Tol_Sym_lm.res)
Tol_Sym_lm.res$EtaSq<-c(eta_squared(Tol_Sym_lm, partial=FALSE)$Eta2, NA)
Tol_Sym_lm.res$Response<-rep("Symbionts", nrow(Tol_Sym_lm.res))
Tol_Sym_lm.res<-Tol_Sym_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

Strong influence of Timepoint, including interactions with main variables of interest, Site and Genotype. Will analyze each Timepoint individually.

Sym Timepoint W2

Symbiont density was not measured at W1, starting with W2 for this response.

Run Model

##Check normality
hist(TolData_W2$Sym.prop)

shapiro.test(TolData_W2$Sym.prop)

    Shapiro-Wilk normality test

data:  TolData_W2$Sym.prop
W = 0.93605, p-value = 0.1477
#Normal

##Model as a function of Site and Genotype
Tol_Sym_W2_lm<-lm(Sym.prop~Site+Genotype+Site:Genotype, data=TolData_W2)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Sym_W2_lm)))


#Q-Q plot
qqnorm(resid(Tol_Sym_W2_lm)); qqline(resid(Tol_Sym_W2_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Sym_W2_lm), resid(Tol_Sym_W2_lm))

Model Results

Overall

#Model Results
summary(Tol_Sym_W2_lm)

Call:
lm(formula = Sym.prop ~ Site + Genotype + Site:Genotype, data = TolData_W2)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.26060 -0.11145  0.00000  0.08525  0.28580 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.5237625  0.0325072  16.112 9.90e-12 ***
Site.L             0.0939568  0.0459722   2.044   0.0568 .  
Genotype.L        -0.4152131  0.0570402  -7.279 1.29e-06 ***
Genotype.Q        -0.0005205  0.0555584  -0.009   0.9926    
Site.L:Genotype.L -0.2061000  0.0806671  -2.555   0.0205 *  
Site.L:Genotype.Q  0.0462891  0.0785715   0.589   0.5635    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.155 on 17 degrees of freedom
Multiple R-squared:  0.7924,    Adjusted R-squared:  0.7313 
F-statistic: 12.98 on 5 and 17 DF,  p-value: 2.647e-05
anova(Tol_Sym_W2_lm)
Analysis of Variance Table

Response: Sym.prop
              Df  Sum Sq Mean Sq F value    Pr(>F)    
Site           1 0.16148 0.16148  6.7208   0.01897 *  
Genotype       2 1.22843 0.61421 25.5640 7.508e-06 ***
Site:Genotype  2 0.16883 0.08441  3.5133   0.05283 .  
Residuals     17 0.40845 0.02403                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Effect Size of Predictors
eta_squared(Tol_Sym_W2_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter     | Eta2 |       95% CI
-----------------------------------
Site          | 0.08 | [0.00, 1.00]
Genotype      | 0.62 | [0.33, 1.00]
Site:Genotype | 0.09 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_Sym_W2_lm.res<-data.frame(anova(Tol_Sym_W2_lm))
Tol_Sym_W2_lm.res$Predictor<-rownames(Tol_Sym_W2_lm.res)
Tol_Sym_W2_lm.res$EtaSq<-c(eta_squared(Tol_Sym_W2_lm, partial=FALSE)$Eta2, NA)
Tol_Sym_W2_lm.res$Response<-rep("Symbionts", nrow(Tol_Sym_W2_lm.res))
Tol_Sym_W2_lm.res$TimeP<-rep("W2", nrow(Tol_Sym_W2_lm.res))
Tol_Sym_W2_lm.res<-Tol_Sym_W2_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

Significant effect of Site and Genotype. Marginal effect (p<0.1) of Site * Genotype. Still checking Site*Genotype for comparability across Timepoints.

Pairwise

#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_Sym_W2_lm, pairwise~Genotype | Site)
$emmeans
Site = KL:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.634 0.0775 17   0.4708    0.798
 AC12      0.484 0.0775 17   0.3210    0.648
 AC8       0.253 0.0775 17   0.0897    0.417

Site = SS:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      1.000 0.0775 17   0.8365    1.164
 AC12      0.564 0.0775 17   0.4004    0.727
 AC8       0.207 0.0895 17   0.0179    0.396

Confidence level used: 0.95 

$contrasts
Site = KL:
 contrast    estimate    SE df t.ratio p.value
 AC10 - AC12    0.150 0.110 17   1.367  0.3795
 AC10 - AC8     0.381 0.110 17   3.477  0.0077
 AC12 - AC8     0.231 0.110 17   2.110  0.1174

Site = SS:
 contrast    estimate    SE df t.ratio p.value
 AC10 - AC12    0.436 0.110 17   3.979  0.0026
 AC10 - AC8     0.793 0.118 17   6.701  <.0001
 AC12 - AC8     0.357 0.118 17   3.017  0.0201

P value adjustment: tukey method for comparing a family of 3 estimates 
#Sites within Genotypes
emmeans(Tol_Sym_W2_lm, pairwise~Site | Genotype)
$emmeans
Genotype = AC10:
 Site emmean     SE df lower.CL upper.CL
 KL    0.634 0.0775 17   0.4708    0.798
 SS    1.000 0.0775 17   0.8365    1.164

Genotype = AC12:
 Site emmean     SE df lower.CL upper.CL
 KL    0.484 0.0775 17   0.3210    0.648
 SS    0.564 0.0775 17   0.4004    0.727

Genotype = AC8:
 Site emmean     SE df lower.CL upper.CL
 KL    0.253 0.0775 17   0.0897    0.417
 SS    0.207 0.0895 17   0.0179    0.396

Confidence level used: 0.95 

$contrasts
Genotype = AC10:
 contrast estimate    SE df t.ratio p.value
 KL - SS   -0.3657 0.110 17  -3.337  0.0039

Genotype = AC12:
 contrast estimate    SE df t.ratio p.value
 KL - SS   -0.0794 0.110 17  -0.725  0.4785

Genotype = AC8:
 contrast estimate    SE df t.ratio p.value
 KL - SS    0.0465 0.118 17   0.393  0.6994
##Save p-values

#Genotypes within Sites
Tol_Sym_W2_lm.geno<-data.frame(emmeans(Tol_Sym_W2_lm, pairwise~Genotype | Site)$contrasts)
Tol_Sym_W2_lm.geno<-Tol_Sym_W2_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Sym_W2_lm.geno$group1<-paste(Tol_Sym_W2_lm.geno$Site, Tol_Sym_W2_lm.geno$group1, sep="_")
Tol_Sym_W2_lm.geno$group2<-paste(Tol_Sym_W2_lm.geno$Site, Tol_Sym_W2_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_Sym_W2_lm.site<-data.frame(emmeans(Tol_Sym_W2_lm, pairwise~Site | Genotype)$contrasts)
Tol_Sym_W2_lm.site<-Tol_Sym_W2_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Sym_W2_lm.site$group1<-paste(Tol_Sym_W2_lm.site$group1, Tol_Sym_W2_lm.site$Genotype, sep="_")
Tol_Sym_W2_lm.site$group2<-paste(Tol_Sym_W2_lm.site$group2, Tol_Sym_W2_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_Sym_W2_lm.p<-rbind(Tol_Sym_W2_lm.geno[,c(1:2,4:8)], Tol_Sym_W2_lm.site[,c(1:2,4:8)])
Tol_Sym_W2_lm.p<-Tol_Sym_W2_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_Sym_W2_lm.p$Sig<-ifelse(Tol_Sym_W2_lm.p$p<0.001, "***", ifelse(Tol_Sym_W2_lm.p$p<0.01, "**", ifelse(Tol_Sym_W2_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_Sym_W2_lm.p$Response<-rep("Symbionts", nrow(Tol_Sym_W2_lm.p))
Tol_Sym_W2_lm.p$TimeP<-rep("W2", nrow(Tol_Sym_W2_lm.p))

Plot Retention by Site and Genotype

##Summary statistics by Site and Genotype
Tol_Sym_W2_SG<-summarySE(TolData_W2, measurevar="Sym.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_Sym_W2_SG.plot<-ggplot(Tol_Sym_W2_SG, aes(x=Site.Geno, y=Sym.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Symbiont Retention")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_Sym_W2_lm.p,  y.position=0.95, step.increase=0.15, label="Sig", hide.ns=TRUE); Tol_Sym_W2_SG.plot

Sym Timepoint M1

Run Model

##Check normality
hist(TolData_M1$Sym.prop)

shapiro.test(TolData_M1$Sym.prop)

    Shapiro-Wilk normality test

data:  TolData_M1$Sym.prop
W = 0.85748, p-value = 0.004618
#Not normal

##Try log+1 transformation
hist(log(TolData_M1$Sym.prop+1))

shapiro.test(log(TolData_M1$Sym.prop+1))

    Shapiro-Wilk normality test

data:  log(TolData_M1$Sym.prop + 1)
W = 0.87335, p-value = 0.009041
#Normal

##Model as a function of Site and Genotype
##Model with log transformation
Tol_Sym_M1_lm<-lm(log(Sym.prop+1)~Site+Genotype+Site:Genotype, data=TolData_M1)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Sym_M1_lm)))


#Q-Q plot
qqnorm(resid(Tol_Sym_M1_lm)); qqline(resid(Tol_Sym_M1_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Sym_M1_lm), resid(Tol_Sym_M1_lm))

Model Results

Overall

#Model Results
summary(Tol_Sym_M1_lm)

Call:
lm(formula = log(Sym.prop + 1) ~ Site + Genotype + Site:Genotype, 
    data = TolData_M1)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.098079 -0.055799 -0.004888  0.043331  0.151113 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.307716   0.016556  18.587 2.95e-12 ***
Site.L             0.027767   0.023413   1.186 0.252954    
Genotype.L        -0.295494   0.027204 -10.862 8.59e-09 ***
Genotype.Q         0.129387   0.030075   4.302 0.000548 ***
Site.L:Genotype.L  0.080682   0.038472   2.097 0.052224 .  
Site.L:Genotype.Q -0.005572   0.042532  -0.131 0.897412    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.07694 on 16 degrees of freedom
Multiple R-squared:  0.8989,    Adjusted R-squared:  0.8673 
F-statistic: 28.46 on 5 and 16 DF,  p-value: 2e-07
anova(Tol_Sym_M1_lm)
Analysis of Variance Table

Response: log(Sym.prop + 1)
              Df  Sum Sq Mean Sq F value    Pr(>F)    
Site           1 0.00823 0.00823  1.3902    0.2556    
Genotype       2 0.80811 0.40406 68.2486 1.468e-08 ***
Site:Genotype  2 0.02614 0.01307  2.2076    0.1423    
Residuals     16 0.09473 0.00592                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Effect Size of Predictors
eta_squared(Tol_Sym_M1_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter     |     Eta2 |       95% CI
---------------------------------------
Site          | 8.78e-03 | [0.00, 1.00]
Genotype      |     0.86 | [0.73, 1.00]
Site:Genotype |     0.03 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_Sym_M1_lm.res<-data.frame(anova(Tol_Sym_M1_lm))
Tol_Sym_M1_lm.res$Predictor<-rownames(Tol_Sym_M1_lm.res)
Tol_Sym_M1_lm.res$EtaSq<-c(eta_squared(Tol_Sym_M1_lm, partial=FALSE)$Eta2, NA)
Tol_Sym_M1_lm.res$Response<-rep("Symbionts", nrow(Tol_Sym_M1_lm.res))
Tol_Sym_M1_lm.res$TimeP<-rep("M1", nrow(Tol_Sym_M1_lm.res))
Tol_Sym_M1_lm.res<-Tol_Sym_M1_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

Significant effect of Genotype. Still checking Site*Genotype for comparability across Timepoints.

Pairwise

#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_Sym_M1_lm, pairwise~Genotype | Site)
$emmeans
Site = KL:
 Genotype emmean     SE df lower.CL upper.CL
 AC10     0.5918 0.0385 16   0.5102    0.673
 AC12     0.1792 0.0444 16   0.0850    0.273
 AC8      0.0932 0.0385 16   0.0117    0.175

Site = SS:
 Genotype emmean     SE df lower.CL upper.CL
 AC10     0.5472 0.0385 16   0.4656    0.629
 AC12     0.2249 0.0444 16   0.1307    0.319
 AC8      0.2100 0.0385 16   0.1284    0.292

Results are given on the log(mu + 1) (not the response) scale. 
Confidence level used: 0.95 

$contrasts
Site = KL:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12    0.413 0.0588 16   7.021  <.0001
 AC10 - AC8     0.499 0.0544 16   9.164  <.0001
 AC12 - AC8     0.086 0.0588 16   1.463  0.3339

Site = SS:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12    0.322 0.0588 16   5.483  0.0001
 AC10 - AC8     0.337 0.0544 16   6.198  <.0001
 AC12 - AC8     0.015 0.0588 16   0.255  0.9650

Note: contrasts are still on the log(mu + 1) scale 
P value adjustment: tukey method for comparing a family of 3 estimates 
#Sites within Genotypes
emmeans(Tol_Sym_M1_lm, pairwise~Site | Genotype)
$emmeans
Genotype = AC10:
 Site emmean     SE df lower.CL upper.CL
 KL   0.5918 0.0385 16   0.5102    0.673
 SS   0.5472 0.0385 16   0.4656    0.629

Genotype = AC12:
 Site emmean     SE df lower.CL upper.CL
 KL   0.1792 0.0444 16   0.0850    0.273
 SS   0.2249 0.0444 16   0.1307    0.319

Genotype = AC8:
 Site emmean     SE df lower.CL upper.CL
 KL   0.0932 0.0385 16   0.0117    0.175
 SS   0.2100 0.0385 16   0.1284    0.292

Results are given on the log(mu + 1) (not the response) scale. 
Confidence level used: 0.95 

$contrasts
Genotype = AC10:
 contrast estimate     SE df t.ratio p.value
 KL - SS    0.0446 0.0544 16   0.820  0.4241

Genotype = AC12:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.0457 0.0628 16  -0.727  0.4775

Genotype = AC8:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.1167 0.0544 16  -2.146  0.0476

Note: contrasts are still on the log(mu + 1) scale 
##Save p-values

#Genotypes within Sites
Tol_Sym_M1_lm.geno<-data.frame(emmeans(Tol_Sym_M1_lm, pairwise~Genotype | Site)$contrasts)
Tol_Sym_M1_lm.geno<-Tol_Sym_M1_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Sym_M1_lm.geno$group1<-paste(Tol_Sym_M1_lm.geno$Site, Tol_Sym_M1_lm.geno$group1, sep="_")
Tol_Sym_M1_lm.geno$group2<-paste(Tol_Sym_M1_lm.geno$Site, Tol_Sym_M1_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_Sym_M1_lm.site<-data.frame(emmeans(Tol_Sym_M1_lm, pairwise~Site | Genotype)$contrasts)
Tol_Sym_M1_lm.site<-Tol_Sym_M1_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Sym_M1_lm.site$group1<-paste(Tol_Sym_M1_lm.site$group1, Tol_Sym_M1_lm.site$Genotype, sep="_")
Tol_Sym_M1_lm.site$group2<-paste(Tol_Sym_M1_lm.site$group2, Tol_Sym_M1_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_Sym_M1_lm.p<-rbind(Tol_Sym_M1_lm.geno[,c(1:2,4:8)], Tol_Sym_M1_lm.site[,c(1:2,4:8)])
Tol_Sym_M1_lm.p<-Tol_Sym_M1_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_Sym_M1_lm.p$Sig<-ifelse(Tol_Sym_M1_lm.p$p<0.001, "***", ifelse(Tol_Sym_M1_lm.p$p<0.01, "**", ifelse(Tol_Sym_M1_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_Sym_M1_lm.p$Response<-rep("Symbionts", nrow(Tol_Sym_M1_lm.p))
Tol_Sym_M1_lm.p$TimeP<-rep("M1", nrow(Tol_Sym_M1_lm.p))

Plot Retention by Site and Genotype

##Summary statistics by Site and Genotype
Tol_Sym_M1_SG<-summarySE(TolData_M1, measurevar="Sym.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_Sym_M1_SG.plot<-ggplot(Tol_Sym_M1_SG, aes(x=Site.Geno, y=Sym.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Symbiont Retention")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
 ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_Sym_M1_lm.p,  y.position=0.95, step.increase=0.15, label="Sig", hide.ns=TRUE); Tol_Sym_M1_SG.plot

Sym Timepoint M4

Run Model

##Check normality
hist(TolData_M4$Sym.prop)

shapiro.test(TolData_M4$Sym.prop)

    Shapiro-Wilk normality test

data:  TolData_M4$Sym.prop
W = 0.88557, p-value = 0.01078
#Not Normal

##Try square transformation
hist((TolData_M4$Sym.prop)^2)

shapiro.test((TolData_M4$Sym.prop)^2)

    Shapiro-Wilk normality test

data:  (TolData_M4$Sym.prop)^2
W = 0.87973, p-value = 0.008208
#Not Normal

##Try cubed transformation
hist((TolData_M4$Sym.prop)^3)

shapiro.test((TolData_M4$Sym.prop)^3)

    Shapiro-Wilk normality test

data:  (TolData_M4$Sym.prop)^3
W = 0.8488, p-value = 0.002077
#Not Normal

##Model as a function of Site and Genotype
##Model with no transformation and check residuals
Tol_Sym_M4_lm<-lm(Sym.prop~Site+Genotype+Site:Genotype, data=TolData_M4)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Sym_M4_lm)))


#Q-Q plot
qqnorm(resid(Tol_Sym_M4_lm)); qqline(resid(Tol_Sym_M4_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Sym_M4_lm), resid(Tol_Sym_M4_lm))

Model Results

Overall

#Model Results
summary(Tol_Sym_M4_lm)

Call:
lm(formula = Sym.prop ~ Site + Genotype + Site:Genotype, data = TolData_M4)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.31820 -0.08804  0.00000  0.08618  0.29740 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.71662    0.03157  22.702 1.07e-14 ***
Site.L            -0.12617    0.04464  -2.826   0.0112 *  
Genotype.L        -0.28587    0.05467  -5.229 5.67e-05 ***
Genotype.Q        -0.11565    0.05467  -2.115   0.0486 *  
Site.L:Genotype.L -0.09494    0.07732  -1.228   0.2353    
Site.L:Genotype.Q  0.17275    0.07732   2.234   0.0384 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1546 on 18 degrees of freedom
Multiple R-squared:  0.7201,    Adjusted R-squared:  0.6423 
F-statistic:  9.26 on 5 and 18 DF,  p-value: 0.0001686
anova(Tol_Sym_M4_lm)
Analysis of Variance Table

Response: Sym.prop
              Df  Sum Sq Mean Sq F value   Pr(>F)    
Site           1 0.19101 0.19101  7.9874 0.011190 *  
Genotype       2 0.76080 0.38040 15.9068 0.000105 ***
Site:Genotype  2 0.15542 0.07771  3.2496 0.062386 .  
Residuals     18 0.43046 0.02391                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Effect Size of Predictors
eta_squared(Tol_Sym_M4_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter     | Eta2 |       95% CI
-----------------------------------
Site          | 0.12 | [0.00, 1.00]
Genotype      | 0.49 | [0.18, 1.00]
Site:Genotype | 0.10 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_Sym_M4_lm.res<-data.frame(anova(Tol_Sym_M4_lm))
Tol_Sym_M4_lm.res$Predictor<-rownames(Tol_Sym_M4_lm.res)
Tol_Sym_M4_lm.res$EtaSq<-c(eta_squared(Tol_Sym_M4_lm, partial=FALSE)$Eta2, NA)
Tol_Sym_M4_lm.res$Response<-rep("Symbionts", nrow(Tol_Sym_M4_lm.res))
Tol_Sym_M4_lm.res$TimeP<-rep("M4", nrow(Tol_Sym_M4_lm.res))
Tol_Sym_M4_lm.res<-Tol_Sym_M4_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

Significant effect of Site and Genotype. Marginal effect (p<0.1) of Site * Genotype. Still checking Site*Genotype for comparability across Timepoints.

Pairwise

#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_Sym_M4_lm, pairwise~Genotype | Site)
$emmeans
Site = KL:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.863 0.0773 18    0.701    1.026
 AC12      1.000 0.0773 18    0.838    1.162
 AC8       0.554 0.0773 18    0.392    0.717

Site = SS:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.880 0.0773 18    0.717    1.042
 AC12      0.622 0.0773 18    0.460    0.785
 AC8       0.380 0.0773 18    0.218    0.543

Confidence level used: 0.95 

$contrasts
Site = KL:
 contrast    estimate    SE df t.ratio p.value
 AC10 - AC12   -0.137 0.109 18  -1.249  0.4409
 AC10 - AC8     0.309 0.109 18   2.829  0.0285
 AC12 - AC8     0.446 0.109 18   4.078  0.0019

Site = SS:
 contrast    estimate    SE df t.ratio p.value
 AC10 - AC12    0.258 0.109 18   2.356  0.0734
 AC10 - AC8     0.499 0.109 18   4.565  0.0007
 AC12 - AC8     0.242 0.109 18   2.210  0.0965

P value adjustment: tukey method for comparing a family of 3 estimates 
#Sites within Genotypes
emmeans(Tol_Sym_M4_lm, pairwise~Site | Genotype)
$emmeans
Genotype = AC10:
 Site emmean     SE df lower.CL upper.CL
 KL    0.863 0.0773 18    0.701    1.026
 SS    0.880 0.0773 18    0.717    1.042

Genotype = AC12:
 Site emmean     SE df lower.CL upper.CL
 KL    1.000 0.0773 18    0.838    1.162
 SS    0.622 0.0773 18    0.460    0.785

Genotype = AC8:
 Site emmean     SE df lower.CL upper.CL
 KL    0.554 0.0773 18    0.392    0.717
 SS    0.380 0.0773 18    0.218    0.543

Confidence level used: 0.95 

$contrasts
Genotype = AC10:
 contrast estimate    SE df t.ratio p.value
 KL - SS   -0.0163 0.109 18  -0.149  0.8835

Genotype = AC12:
 contrast estimate    SE df t.ratio p.value
 KL - SS    0.3779 0.109 18   3.456  0.0028

Genotype = AC8:
 contrast estimate    SE df t.ratio p.value
 KL - SS    0.1736 0.109 18   1.588  0.1297
##Save p-values

#Genotypes within Sites
Tol_Sym_M4_lm.geno<-data.frame(emmeans(Tol_Sym_M4_lm, pairwise~Genotype | Site)$contrasts)
Tol_Sym_M4_lm.geno<-Tol_Sym_M4_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Sym_M4_lm.geno$group1<-paste(Tol_Sym_M4_lm.geno$Site, Tol_Sym_M4_lm.geno$group1, sep="_")
Tol_Sym_M4_lm.geno$group2<-paste(Tol_Sym_M4_lm.geno$Site, Tol_Sym_M4_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_Sym_M4_lm.site<-data.frame(emmeans(Tol_Sym_M4_lm, pairwise~Site | Genotype)$contrasts)
Tol_Sym_M4_lm.site<-Tol_Sym_M4_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Sym_M4_lm.site$group1<-paste(Tol_Sym_M4_lm.site$group1, Tol_Sym_M4_lm.site$Genotype, sep="_")
Tol_Sym_M4_lm.site$group2<-paste(Tol_Sym_M4_lm.site$group2, Tol_Sym_M4_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_Sym_M4_lm.p<-rbind(Tol_Sym_M4_lm.geno[,c(1:2,4:8)], Tol_Sym_M4_lm.site[,c(1:2,4:8)])
Tol_Sym_M4_lm.p<-Tol_Sym_M4_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_Sym_M4_lm.p$Sig<-ifelse(Tol_Sym_M4_lm.p$p<0.001, "***", ifelse(Tol_Sym_M4_lm.p$p<0.01, "**", ifelse(Tol_Sym_M4_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_Sym_M4_lm.p$Response<-rep("Symbionts", nrow(Tol_Sym_M4_lm.p))
Tol_Sym_M4_lm.p$TimeP<-rep("M4", nrow(Tol_Sym_M4_lm.p))

Plot Retention by Site and Genotype

##Summary statistics by Site and Genotype
Tol_Sym_M4_SG<-summarySE(TolData_M4, measurevar="Sym.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_Sym_M4_SG.plot<-ggplot(Tol_Sym_M4_SG, aes(x=Site.Geno, y=Sym.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Symbiont Retention")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_Sym_M4_lm.p,  y.position=1.1, step.increase=0.15, label="Sig", hide.ns=TRUE); Tol_Sym_M4_SG.plot

Sym Timepoint M8

Run Model

##Check normality
hist(TolData_M8$Sym.prop)

shapiro.test(TolData_M8$Sym.prop)

    Shapiro-Wilk normality test

data:  TolData_M8$Sym.prop
W = 0.88171, p-value = 0.008995
#Not Normal

##Try square transformation
hist((TolData_M8$Sym.prop)^2)

shapiro.test((TolData_M8$Sym.prop)^2)

    Shapiro-Wilk normality test

data:  (TolData_M8$Sym.prop)^2
W = 0.88024, p-value = 0.008402
#Not Normal

##Try cubed transformation
hist((TolData_M8$Sym.prop)^3)

shapiro.test((TolData_M8$Sym.prop)^3)

    Shapiro-Wilk normality test

data:  (TolData_M8$Sym.prop)^3
W = 0.8729, p-value = 0.005999
#Not Normal

##Model as a function of Site and Genotype
##Model with no transformation and check residuals
Tol_Sym_M8_lm<-lm(Sym.prop~Site+Genotype+Site:Genotype, data=TolData_M8)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Sym_M8_lm)))


#Q-Q plot
qqnorm(resid(Tol_Sym_M8_lm)); qqline(resid(Tol_Sym_M8_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Sym_M8_lm), resid(Tol_Sym_M8_lm))

Model Results

Overall

#Model Results
summary(Tol_Sym_M8_lm)

Call:
lm(formula = Sym.prop ~ Site + Genotype + Site:Genotype, data = TolData_M8)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.157800 -0.050981  0.001975  0.059175  0.151250 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.87608    0.01989  44.048   <2e-16 ***
Site.L             0.07647    0.02813   2.719   0.0141 *  
Genotype.L         0.04907    0.03445   1.425   0.1714    
Genotype.Q        -0.02291    0.03445  -0.665   0.5144    
Site.L:Genotype.L -0.11457    0.04872  -2.352   0.0303 *  
Site.L:Genotype.Q  0.01391    0.04872   0.286   0.7784    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.09744 on 18 degrees of freedom
Multiple R-squared:  0.4623,    Adjusted R-squared:  0.3129 
F-statistic: 3.095 on 5 and 18 DF,  p-value: 0.03448
anova(Tol_Sym_M8_lm)
Analysis of Variance Table

Response: Sym.prop
              Df   Sum Sq  Mean Sq F value  Pr(>F)  
Site           1 0.070168 0.070168  7.3908 0.01408 *
Genotype       2 0.023465 0.011733  1.2358 0.31411  
Site:Genotype  2 0.053284 0.026642  2.8062 0.08693 .
Residuals     18 0.170890 0.009494                  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Effect Size of Predictors
eta_squared(Tol_Sym_M8_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter     | Eta2 |       95% CI
-----------------------------------
Site          | 0.22 | [0.01, 1.00]
Genotype      | 0.07 | [0.00, 1.00]
Site:Genotype | 0.17 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_Sym_M8_lm.res<-data.frame(anova(Tol_Sym_M8_lm))
Tol_Sym_M8_lm.res$Predictor<-rownames(Tol_Sym_M8_lm.res)
Tol_Sym_M8_lm.res$EtaSq<-c(eta_squared(Tol_Sym_M8_lm, partial=FALSE)$Eta2, NA)
Tol_Sym_M8_lm.res$Response<-rep("Symbionts", nrow(Tol_Sym_M8_lm.res))
Tol_Sym_M8_lm.res$TimeP<-rep("M8", nrow(Tol_Sym_M8_lm.res))
Tol_Sym_M8_lm.res<-Tol_Sym_M8_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

Significant effect of Site. Marginal effect (p<0.1) of Site * Genotype. Still checking Site*Genotype for comparability across Timepoints.

Pairwise

#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_Sym_M8_lm, pairwise~Genotype | Site)
$emmeans
Site = KL:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.717 0.0487 18    0.614    0.819
 AC12      0.849 0.0487 18    0.746    0.951
 AC8       0.901 0.0487 18    0.798    1.003

Site = SS:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.947 0.0487 18    0.845    1.050
 AC12      0.941 0.0487 18    0.838    1.043
 AC8       0.902 0.0487 18    0.800    1.005

Confidence level used: 0.95 

$contrasts
Site = KL:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12 -0.13210 0.0689 18  -1.917  0.1626
 AC10 - AC8  -0.18397 0.0689 18  -2.670  0.0395
 AC12 - AC8  -0.05187 0.0689 18  -0.753  0.7358

Site = SS:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12  0.00658 0.0689 18   0.095  0.9950
 AC10 - AC8   0.04517 0.0689 18   0.656  0.7916
 AC12 - AC8   0.03860 0.0689 18   0.560  0.8426

P value adjustment: tukey method for comparing a family of 3 estimates 
#Sites within Genotypes
emmeans(Tol_Sym_M8_lm, pairwise~Site | Genotype)
$emmeans
Genotype = AC10:
 Site emmean     SE df lower.CL upper.CL
 KL    0.717 0.0487 18    0.614    0.819
 SS    0.947 0.0487 18    0.845    1.050

Genotype = AC12:
 Site emmean     SE df lower.CL upper.CL
 KL    0.849 0.0487 18    0.746    0.951
 SS    0.941 0.0487 18    0.838    1.043

Genotype = AC8:
 Site emmean     SE df lower.CL upper.CL
 KL    0.901 0.0487 18    0.798    1.003
 SS    0.902 0.0487 18    0.800    1.005

Confidence level used: 0.95 

$contrasts
Genotype = AC10:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.2308 0.0689 18  -3.349  0.0036

Genotype = AC12:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.0921 0.0689 18  -1.336  0.1981

Genotype = AC8:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.0016 0.0689 18  -0.023  0.9817
##Save p-values

#Genotypes within Sites
Tol_Sym_M8_lm.geno<-data.frame(emmeans(Tol_Sym_M8_lm, pairwise~Genotype | Site)$contrasts)
Tol_Sym_M8_lm.geno<-Tol_Sym_M8_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Sym_M8_lm.geno$group1<-paste(Tol_Sym_M8_lm.geno$Site, Tol_Sym_M8_lm.geno$group1, sep="_")
Tol_Sym_M8_lm.geno$group2<-paste(Tol_Sym_M8_lm.geno$Site, Tol_Sym_M8_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_Sym_M8_lm.site<-data.frame(emmeans(Tol_Sym_M8_lm, pairwise~Site | Genotype)$contrasts)
Tol_Sym_M8_lm.site<-Tol_Sym_M8_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Sym_M8_lm.site$group1<-paste(Tol_Sym_M8_lm.site$group1, Tol_Sym_M8_lm.site$Genotype, sep="_")
Tol_Sym_M8_lm.site$group2<-paste(Tol_Sym_M8_lm.site$group2, Tol_Sym_M8_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_Sym_M8_lm.p<-rbind(Tol_Sym_M8_lm.geno[,c(1:2,4:8)], Tol_Sym_M8_lm.site[,c(1:2,4:8)])
Tol_Sym_M8_lm.p<-Tol_Sym_M8_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_Sym_M8_lm.p$Sig<-ifelse(Tol_Sym_M8_lm.p$p<0.001, "***", ifelse(Tol_Sym_M8_lm.p$p<0.01, "**", ifelse(Tol_Sym_M8_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_Sym_M8_lm.p$Response<-rep("Symbionts", nrow(Tol_Sym_M8_lm.p))
Tol_Sym_M8_lm.p$TimeP<-rep("M8", nrow(Tol_Sym_M8_lm.p))

Plot Retention by Site and Genotype

##Summary statistics by Site and Genotype
Tol_Sym_M8_SG<-summarySE(TolData_M8, measurevar="Sym.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_Sym_M8_SG.plot<-ggplot(Tol_Sym_M8_SG, aes(x=Site.Geno, y=Sym.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Symbiont Retention")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_Sym_M8_lm.p,  y.position=1.05, step.increase=0.4, label="Sig", hide.ns=TRUE); Tol_Sym_M8_SG.plot

Sym Timepoint M12

Run Model

##Check normality
hist(TolData_M12$Sym.prop)

shapiro.test(TolData_M12$Sym.prop)

    Shapiro-Wilk normality test

data:  TolData_M12$Sym.prop
W = 0.9228, p-value = 0.06737
#Normal

##Model as a function of Site and Genotype
Tol_Sym_M12_lm<-lm(Sym.prop~Site+Genotype+Site:Genotype, data=TolData_M12)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Sym_M12_lm)))


#Q-Q plot
qqnorm(resid(Tol_Sym_M12_lm)); qqline(resid(Tol_Sym_M12_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Sym_M12_lm), resid(Tol_Sym_M12_lm))

Model Results

Overall

#Model Results
summary(Tol_Sym_M12_lm)

Call:
lm(formula = Sym.prop ~ Site + Genotype + Site:Genotype, data = TolData_M12)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.17948 -0.12040 -0.02149  0.05658  0.37585 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.26903    0.03512   7.660 4.52e-07 ***
Site.L            -0.02682    0.04967  -0.540    0.596    
Genotype.L        -0.06026    0.06083  -0.991    0.335    
Genotype.Q         0.05019    0.06083   0.825    0.420    
Site.L:Genotype.L  0.06158    0.08603   0.716    0.483    
Site.L:Genotype.Q -0.04186    0.08603  -0.487    0.632    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1721 on 18 degrees of freedom
Multiple R-squared:  0.1305,    Adjusted R-squared:  -0.111 
F-statistic: 0.5405 on 5 and 18 DF,  p-value: 0.7432
anova(Tol_Sym_M12_lm)
Analysis of Variance Table

Response: Sym.prop
              Df  Sum Sq   Mean Sq F value Pr(>F)
Site           1 0.00863 0.0086336  0.2916 0.5958
Genotype       2 0.04921 0.0246044  0.8311 0.4516
Site:Genotype  2 0.02217 0.0110871  0.3745 0.6929
Residuals     18 0.53291 0.0296063               
#Effect Size of Predictors
eta_squared(Tol_Sym_M12_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter     | Eta2 |       95% CI
-----------------------------------
Site          | 0.01 | [0.00, 1.00]
Genotype      | 0.08 | [0.00, 1.00]
Site:Genotype | 0.04 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_Sym_M12_lm.res<-data.frame(anova(Tol_Sym_M12_lm))
Tol_Sym_M12_lm.res$Predictor<-rownames(Tol_Sym_M12_lm.res)
Tol_Sym_M12_lm.res$EtaSq<-c(eta_squared(Tol_Sym_M12_lm, partial=FALSE)$Eta2, NA)
Tol_Sym_M12_lm.res$Response<-rep("Symbionts", nrow(Tol_Sym_M12_lm.res))
Tol_Sym_M12_lm.res$TimeP<-rep("M12", nrow(Tol_Sym_M12_lm.res))
Tol_Sym_M12_lm.res<-Tol_Sym_M12_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

No significant effects of Site or Genotype. Still checking Site*Genotype for comparability across Timepoints.

Pairwise

#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_Sym_M12_lm, pairwise~Genotype | Site)
$emmeans
Site = KL:
 Genotype emmean    SE df lower.CL upper.CL
 AC10      0.394 0.086 18   0.2132    0.575
 AC12      0.223 0.086 18   0.0421    0.404
 AC8       0.247 0.086 18   0.0664    0.428

Site = SS:
 Genotype emmean    SE df lower.CL upper.CL
 AC10      0.270 0.086 18   0.0896    0.451
 AC12      0.233 0.086 18   0.0525    0.414
 AC8       0.247 0.086 18   0.0659    0.427

Confidence level used: 0.95 

$contrasts
Site = KL:
 contrast    estimate    SE df t.ratio p.value
 AC10 - AC12   0.1711 0.122 18   1.406  0.3586
 AC10 - AC8    0.1468 0.122 18   1.207  0.4646
 AC12 - AC8   -0.0243 0.122 18  -0.200  0.9782

Site = SS:
 contrast    estimate    SE df t.ratio p.value
 AC10 - AC12   0.0370 0.122 18   0.305  0.9503
 AC10 - AC8    0.0237 0.122 18   0.194  0.9794
 AC12 - AC8   -0.0134 0.122 18  -0.110  0.9933

P value adjustment: tukey method for comparing a family of 3 estimates 
#Sites within Genotypes
emmeans(Tol_Sym_M12_lm, pairwise~Site | Genotype)
$emmeans
Genotype = AC10:
 Site emmean    SE df lower.CL upper.CL
 KL    0.394 0.086 18   0.2132    0.575
 SS    0.270 0.086 18   0.0896    0.451

Genotype = AC12:
 Site emmean    SE df lower.CL upper.CL
 KL    0.223 0.086 18   0.0421    0.404
 SS    0.233 0.086 18   0.0525    0.414

Genotype = AC8:
 Site emmean    SE df lower.CL upper.CL
 KL    0.247 0.086 18   0.0664    0.428
 SS    0.247 0.086 18   0.0659    0.427

Confidence level used: 0.95 

$contrasts
Genotype = AC10:
 contrast  estimate    SE df t.ratio p.value
 KL - SS   0.123675 0.122 18   1.016  0.3229

Genotype = AC12:
 contrast  estimate    SE df t.ratio p.value
 KL - SS  -0.010400 0.122 18  -0.085  0.9328

Genotype = AC8:
 contrast  estimate    SE df t.ratio p.value
 KL - SS   0.000525 0.122 18   0.004  0.9966
##Save p-values

#Genotypes within Sites
Tol_Sym_M12_lm.geno<-data.frame(emmeans(Tol_Sym_M12_lm, pairwise~Genotype | Site)$contrasts)
Tol_Sym_M12_lm.geno<-Tol_Sym_M12_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Sym_M12_lm.geno$group1<-paste(Tol_Sym_M12_lm.geno$Site, Tol_Sym_M12_lm.geno$group1, sep="_")
Tol_Sym_M12_lm.geno$group2<-paste(Tol_Sym_M12_lm.geno$Site, Tol_Sym_M12_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_Sym_M12_lm.site<-data.frame(emmeans(Tol_Sym_M12_lm, pairwise~Site | Genotype)$contrasts)
Tol_Sym_M12_lm.site<-Tol_Sym_M12_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Sym_M12_lm.site$group1<-paste(Tol_Sym_M12_lm.site$group1, Tol_Sym_M12_lm.site$Genotype, sep="_")
Tol_Sym_M12_lm.site$group2<-paste(Tol_Sym_M12_lm.site$group2, Tol_Sym_M12_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_Sym_M12_lm.p<-rbind(Tol_Sym_M12_lm.geno[,c(1:2,4:8)], Tol_Sym_M12_lm.site[,c(1:2,4:8)])
Tol_Sym_M12_lm.p<-Tol_Sym_M12_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_Sym_M12_lm.p$Sig<-ifelse(Tol_Sym_M12_lm.p$p<0.001, "***", ifelse(Tol_Sym_M12_lm.p$p<0.01, "**", ifelse(Tol_Sym_M12_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_Sym_M12_lm.p$Response<-rep("Symbionts", nrow(Tol_Sym_M12_lm.p))
Tol_Sym_M12_lm.p$TimeP<-rep("M12", nrow(Tol_Sym_M12_lm.p))

Plot Retention by Site and Genotype

##Summary statistics by Site and Genotype
Tol_Sym_M12_SG<-summarySE(TolData_M12, measurevar="Sym.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_Sym_M12_SG.plot<-ggplot(Tol_Sym_M12_SG, aes(x=Site.Geno, y=Sym.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Symbiont Retention")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o); Tol_Sym_M12_SG.plot


#+ stat_pvalue_manual(data=Tol_Sym_M12_lm.p,  y.position=0.95, step.increase=0.15, label="Sig", hide.ns=TRUE) #No significant differences

Thermal Tolerance Color Full Set

Full Set

Run Model

##Check normality
hist(TolData$Score_Full.prop)

shapiro.test(TolData$Score_Full.prop)

    Shapiro-Wilk normality test

data:  TolData$Score_Full.prop
W = 0.90787, p-value = 8.714e-08
#Not Normal

##Try square transformation
hist((TolData$Score_Full.prop)^2)

shapiro.test((TolData$Score_Full.prop)^2)

    Shapiro-Wilk normality test

data:  (TolData$Score_Full.prop)^2
W = 0.90167, p-value = 3.893e-08
#Not normal 

##Try cubed transformation
hist((TolData$Score_Full.prop)^3)

shapiro.test((TolData$Score_Full.prop)^3)

    Shapiro-Wilk normality test

data:  (TolData$Score_Full.prop)^3
W = 0.87565, p-value = 1.792e-09
#Not normal 


##Model as a function of Site and Genotype and Timepoint
##Model with no transformation and check residuals
Tol_ScoreF_lm<-lm(Score_Full.prop~Site+Genotype+TimeP+ Site:Genotype + Site:TimeP + Genotype:TimeP, data=TolData)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreF_lm)))


#Q-Q plot
qqnorm(resid(Tol_ScoreF_lm)); qqline(resid(Tol_ScoreF_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreF_lm), resid(Tol_ScoreF_lm))

Residuals are not great, need to check for other modeling options for Color Full Set.

Model Results

#Model Results
summary(Tol_ScoreF_lm)

Call:
lm(formula = Score_Full.prop ~ Site + Genotype + TimeP + Site:Genotype + 
    Site:TimeP + Genotype:TimeP, data = TolData)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.39018 -0.04431 -0.00316  0.05485  0.44471 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         0.714814   0.011766  60.755  < 2e-16 ***
Site.L              0.057324   0.016618   3.449 0.000788 ***
Genotype.L         -0.165762   0.020155  -8.224 3.54e-13 ***
Genotype.Q         -0.017805   0.020604  -0.864 0.389333    
TimeP.L             0.172504   0.028691   6.012 2.25e-08 ***
TimeP.Q            -0.286969   0.028843  -9.950  < 2e-16 ***
TimeP.C            -0.224989   0.028759  -7.823 2.86e-12 ***
TimeP^4            -0.071363   0.028803  -2.478 0.014693 *  
TimeP^5            -0.053084   0.029014  -1.830 0.069924 .  
Site.L:Genotype.L   0.030948   0.028503   1.086 0.279871    
Site.L:Genotype.Q  -0.001701   0.029078  -0.059 0.953441    
Site.L:TimeP.L     -0.074957   0.040574  -1.847 0.067277 .  
Site.L:TimeP.Q     -0.045864   0.040708  -1.127 0.262255    
Site.L:TimeP.C      0.054853   0.040629   1.350 0.179657    
Site.L:TimeP^4     -0.021594   0.040696  -0.531 0.596720    
Site.L:TimeP^5      0.003590   0.040907   0.088 0.930220    
Genotype.L:TimeP.L  0.277820   0.049298   5.635 1.28e-07 ***
Genotype.Q:TimeP.L  0.080240   0.050101   1.602 0.112021    
Genotype.L:TimeP.Q  0.030629   0.049080   0.624 0.533835    
Genotype.Q:TimeP.Q -0.084229   0.050814  -1.658 0.100149    
Genotype.L:TimeP.C -0.034948   0.049565  -0.705 0.482187    
Genotype.Q:TimeP.C  0.051244   0.050045   1.024 0.308025    
Genotype.L:TimeP^4 -0.046076   0.049656  -0.928 0.355418    
Genotype.Q:TimeP^4  0.078285   0.050113   1.562 0.121017    
Genotype.L:TimeP^5 -0.034519   0.049244  -0.701 0.484742    
Genotype.Q:TimeP^5 -0.107154   0.051244  -2.091 0.038745 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1388 on 114 degrees of freedom
Multiple R-squared:  0.7526,    Adjusted R-squared:  0.6983 
F-statistic: 13.87 on 25 and 114 DF,  p-value: < 2.2e-16
anova(Tol_ScoreF_lm)
Analysis of Variance Table

Response: Score_Full.prop
                Df Sum Sq Mean Sq F value    Pr(>F)    
Site             1 0.2584 0.25839 13.4204 0.0003791 ***
Genotype         2 1.2851 0.64255 33.3728 3.897e-12 ***
TimeP            5 4.0800 0.81599 42.3811 < 2.2e-16 ***
Site:Genotype    2 0.0245 0.01226  0.6365 0.5309976    
Site:TimeP       5 0.1431 0.02862  1.4863 0.1997489    
Genotype:TimeP  10 0.8845 0.08845  4.5940 1.761e-05 ***
Residuals      114 2.1949 0.01925                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Effect Size of Predictors
eta_squared(Tol_ScoreF_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter      |     Eta2 |       95% CI
----------------------------------------
Site           |     0.03 | [0.00, 1.00]
Genotype       |     0.14 | [0.05, 1.00]
TimeP          |     0.46 | [0.34, 1.00]
Site:Genotype  | 2.76e-03 | [0.00, 1.00]
Site:TimeP     |     0.02 | [0.00, 1.00]
Genotype:TimeP |     0.10 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_ScoreF_lm.res<-data.frame(anova(Tol_ScoreF_lm))
Tol_ScoreF_lm.res$Predictor<-rownames(Tol_ScoreF_lm.res)
Tol_ScoreF_lm.res$EtaSq<-c(eta_squared(Tol_ScoreF_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreF_lm.res$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_lm.res))
Tol_ScoreF_lm.res<-Tol_ScoreF_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

Strong influence of Timepoint, including interactions with Genotype. Will analyze each Timepoint individually.

Color Score Full Set Timepoint W1

Run Model

##Check normality
hist(TolData_W1$Score_Full.prop)

shapiro.test(TolData_W1$Score_Full.prop)

    Shapiro-Wilk normality test

data:  TolData_W1$Score_Full.prop
W = 0.96698, p-value = 0.6172
#Normal

##Model as a function of Site and Genotype
Tol_ScoreF_W1_lm<-lm(Score_Full.prop~Site+Genotype+Site:Genotype, data=TolData_W1)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreF_W1_lm)))


#Q-Q plot
qqnorm(resid(Tol_ScoreF_W1_lm)); qqline(resid(Tol_ScoreF_W1_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreF_W1_lm), resid(Tol_ScoreF_W1_lm))

Model Results

Overall

#Model Results
summary(Tol_ScoreF_W1_lm)

Call:
lm(formula = Score_Full.prop ~ Site + Genotype + Site:Genotype, 
    data = TolData_W1)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.25028 -0.08754  0.01292  0.07720  0.40030 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.52653    0.03678  14.314 6.49e-11 ***
Site.L             0.04903    0.05202   0.942 0.359175    
Genotype.L        -0.30859    0.06201  -4.976 0.000115 ***
Genotype.Q        -0.10351    0.06537  -1.583 0.131744    
Site.L:Genotype.L  0.02654    0.08770   0.303 0.765870    
Site.L:Genotype.Q  0.07968    0.09244   0.862 0.400713    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1754 on 17 degrees of freedom
Multiple R-squared:  0.6322,    Adjusted R-squared:  0.524 
F-statistic: 5.844 on 5 and 17 DF,  p-value: 0.002556
anova(Tol_ScoreF_W1_lm)
Analysis of Variance Table

Response: Score_Full.prop
              Df  Sum Sq Mean Sq F value    Pr(>F)    
Site           1 0.02487 0.02487  0.8085 0.3811294    
Genotype       2 0.84845 0.42423 13.7892 0.0002762 ***
Site:Genotype  2 0.02567 0.01284  0.4173 0.6654183    
Residuals     17 0.52301 0.03077                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Effect Size of Predictors
eta_squared(Tol_ScoreF_W1_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter     | Eta2 |       95% CI
-----------------------------------
Site          | 0.02 | [0.00, 1.00]
Genotype      | 0.60 | [0.29, 1.00]
Site:Genotype | 0.02 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_ScoreF_W1_lm.res<-data.frame(anova(Tol_ScoreF_W1_lm))
Tol_ScoreF_W1_lm.res$Predictor<-rownames(Tol_ScoreF_W1_lm.res)
Tol_ScoreF_W1_lm.res$EtaSq<-c(eta_squared(Tol_ScoreF_W1_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreF_W1_lm.res$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_W1_lm.res))
Tol_ScoreF_W1_lm.res$TimeP<-rep("W1", nrow(Tol_ScoreF_W1_lm.res))
Tol_ScoreF_W1_lm.res<-Tol_ScoreF_W1_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

Significant effect of Genotype. Still checking Site*Genotype for comparability across Timepoints.

Pairwise

#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreF_W1_lm, pairwise~Genotype | Site)
$emmeans
Site = KL:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.658 0.0877 17   0.4730    0.843
 AC12      0.622 0.0877 17   0.4373    0.807
 AC8       0.195 0.0877 17   0.0101    0.380

Site = SS:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.747 0.0877 17   0.5618    0.932
 AC12      0.600 0.1013 17   0.3860    0.813
 AC8       0.337 0.0877 17   0.1520    0.522

Confidence level used: 0.95 

$contrasts
Site = KL:
 contrast    estimate    SE df t.ratio p.value
 AC10 - AC12   0.0357 0.124 17   0.288  0.9555
 AC10 - AC8    0.4629 0.124 17   3.733  0.0045
 AC12 - AC8    0.4273 0.124 17   3.445  0.0082

Site = SS:
 contrast    estimate    SE df t.ratio p.value
 AC10 - AC12   0.1472 0.134 17   1.099  0.5278
 AC10 - AC8    0.4099 0.124 17   3.305  0.0111
 AC12 - AC8    0.2627 0.134 17   1.961  0.1524

P value adjustment: tukey method for comparing a family of 3 estimates 
#Sites within Genotypes
emmeans(Tol_ScoreF_W1_lm, pairwise~Site | Genotype)
$emmeans
Genotype = AC10:
 Site emmean     SE df lower.CL upper.CL
 KL    0.658 0.0877 17   0.4730    0.843
 SS    0.747 0.0877 17   0.5618    0.932

Genotype = AC12:
 Site emmean     SE df lower.CL upper.CL
 KL    0.622 0.0877 17   0.4373    0.807
 SS    0.600 0.1013 17   0.3860    0.813

Genotype = AC8:
 Site emmean     SE df lower.CL upper.CL
 KL    0.195 0.0877 17   0.0101    0.380
 SS    0.337 0.0877 17   0.1520    0.522

Confidence level used: 0.95 

$contrasts
Genotype = AC10:
 contrast estimate    SE df t.ratio p.value
 KL - SS   -0.0888 0.124 17  -0.716  0.4837

Genotype = AC12:
 contrast estimate    SE df t.ratio p.value
 KL - SS    0.0227 0.134 17   0.169  0.8676

Genotype = AC8:
 contrast estimate    SE df t.ratio p.value
 KL - SS   -0.1419 0.124 17  -1.144  0.2685
##Save p-values

#Genotypes within Sites
Tol_ScoreF_W1_lm.geno<-data.frame(emmeans(Tol_ScoreF_W1_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreF_W1_lm.geno<-Tol_ScoreF_W1_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_W1_lm.geno$group1<-paste(Tol_ScoreF_W1_lm.geno$Site, Tol_ScoreF_W1_lm.geno$group1, sep="_")
Tol_ScoreF_W1_lm.geno$group2<-paste(Tol_ScoreF_W1_lm.geno$Site, Tol_ScoreF_W1_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreF_W1_lm.site<-data.frame(emmeans(Tol_ScoreF_W1_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreF_W1_lm.site<-Tol_ScoreF_W1_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_W1_lm.site$group1<-paste(Tol_ScoreF_W1_lm.site$group1, Tol_ScoreF_W1_lm.site$Genotype, sep="_")
Tol_ScoreF_W1_lm.site$group2<-paste(Tol_ScoreF_W1_lm.site$group2, Tol_ScoreF_W1_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreF_W1_lm.p<-rbind(Tol_ScoreF_W1_lm.geno[,c(1:2,4:8)], Tol_ScoreF_W1_lm.site[,c(1:2,4:8)])
Tol_ScoreF_W1_lm.p<-Tol_ScoreF_W1_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreF_W1_lm.p$Sig<-ifelse(Tol_ScoreF_W1_lm.p$p<0.001, "***", ifelse(Tol_ScoreF_W1_lm.p$p<0.01, "**", ifelse(Tol_ScoreF_W1_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreF_W1_lm.p$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_W1_lm.p))
Tol_ScoreF_W1_lm.p$TimeP<-rep("W1", nrow(Tol_ScoreF_W1_lm.p))

Plot Retention by Site and Genotype

##Summary statistics by Site and Genotype
Tol_ScoreF_W1_SG<-summarySE(TolData_W1, measurevar="Score_Full.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreF_W1_SG.plot<-ggplot(Tol_ScoreF_W1_SG, aes(x=Site.Geno, y=Score_Full.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_Full.prop-se, ymax=Score_Full.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention Full Set")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_ScoreF_W1_lm.p,  y.position=0.80, step.increase=0.2, label="Sig", hide.ns=TRUE); Tol_ScoreF_W1_SG.plot

Color Score Full Set Timepoint W2

Run Model

##Check normality
hist(TolData_W2$Score_Full.prop)

shapiro.test(TolData_W2$Score_Full.prop)

    Shapiro-Wilk normality test

data:  TolData_W2$Score_Full.prop
W = 0.95468, p-value = 0.3647
#Normal

##Model as a function of Site and Genotype
Tol_ScoreF_W2_lm<-lm(Score_Full.prop~Site+Genotype+Site:Genotype, data=TolData_W2)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreF_W2_lm)))


#Q-Q plot
qqnorm(resid(Tol_ScoreF_W2_lm)); qqline(resid(Tol_ScoreF_W2_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreF_W2_lm), resid(Tol_ScoreF_W2_lm))

Model Results

Overall

#Model Results
summary(Tol_ScoreF_W2_lm)

Call:
lm(formula = Score_Full.prop ~ Site + Genotype + Site:Genotype, 
    data = TolData_W2)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.278525 -0.066562 -0.002225  0.055925  0.233075 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.59021    0.03151  18.730 8.72e-13 ***
Site.L             0.13062    0.04456   2.931 0.009326 ** 
Genotype.L        -0.27257    0.05529  -4.930 0.000127 ***
Genotype.Q        -0.08928    0.05386  -1.658 0.115689    
Site.L:Genotype.L  0.00095    0.07820   0.012 0.990448    
Site.L:Genotype.Q  0.02195    0.07616   0.288 0.776645    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1503 on 17 degrees of freedom
Multiple R-squared:  0.6882,    Adjusted R-squared:  0.5965 
F-statistic: 7.505 on 5 and 17 DF,  p-value: 0.0006991
anova(Tol_ScoreF_W2_lm)
Analysis of Variance Table

Response: Score_Full.prop
              Df  Sum Sq  Mean Sq F value    Pr(>F)    
Site           1 0.24105 0.241048 10.6769 0.0045369 ** 
Genotype       2 0.60425 0.302123 13.3821 0.0003231 ***
Site:Genotype  2 0.00188 0.000938  0.0415 0.9594056    
Residuals     17 0.38380 0.022577                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Effect Size of Predictors
eta_squared(Tol_ScoreF_W2_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter     |     Eta2 |       95% CI
---------------------------------------
Site          |     0.20 | [0.00, 1.00]
Genotype      |     0.49 | [0.16, 1.00]
Site:Genotype | 1.52e-03 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_ScoreF_W2_lm.res<-data.frame(anova(Tol_ScoreF_W2_lm))
Tol_ScoreF_W2_lm.res$Predictor<-rownames(Tol_ScoreF_W2_lm.res)
Tol_ScoreF_W2_lm.res$EtaSq<-c(eta_squared(Tol_ScoreF_W2_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreF_W2_lm.res$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_W2_lm.res))
Tol_ScoreF_W2_lm.res$TimeP<-rep("W2", nrow(Tol_ScoreF_W2_lm.res))
Tol_ScoreF_W2_lm.res<-Tol_ScoreF_W2_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

Significant effect of Site and Genotype. Still checking Site*Genotype for comparability across Timepoints.

Pairwise

#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreF_W2_lm, pairwise~Genotype | Site)
$emmeans
Site = KL:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.648 0.0751 17    0.490    0.807
 AC12      0.583 0.0751 17    0.425    0.742
 AC8       0.262 0.0751 17    0.103    0.420

Site = SS:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.845 0.0751 17    0.686    1.003
 AC12      0.743 0.0751 17    0.584    0.901
 AC8       0.460 0.0867 17    0.277    0.643

Confidence level used: 0.95 

$contrasts
Site = KL:
 contrast    estimate    SE df t.ratio p.value
 AC10 - AC12   0.0649 0.106 17   0.610  0.8165
 AC10 - AC8    0.3864 0.106 17   3.637  0.0055
 AC12 - AC8    0.3216 0.106 17   3.027  0.0197

Site = SS:
 contrast    estimate    SE df t.ratio p.value
 AC10 - AC12   0.1019 0.106 17   0.959  0.6115
 AC10 - AC8    0.3845 0.115 17   3.351  0.0100
 AC12 - AC8    0.2826 0.115 17   2.463  0.0610

P value adjustment: tukey method for comparing a family of 3 estimates 
#Sites within Genotypes
emmeans(Tol_ScoreF_W2_lm, pairwise~Site | Genotype)
$emmeans
Genotype = AC10:
 Site emmean     SE df lower.CL upper.CL
 KL    0.648 0.0751 17    0.490    0.807
 SS    0.845 0.0751 17    0.686    1.003

Genotype = AC12:
 Site emmean     SE df lower.CL upper.CL
 KL    0.583 0.0751 17    0.425    0.742
 SS    0.743 0.0751 17    0.584    0.901

Genotype = AC8:
 Site emmean     SE df lower.CL upper.CL
 KL    0.262 0.0751 17    0.103    0.420
 SS    0.460 0.0867 17    0.277    0.643

Confidence level used: 0.95 

$contrasts
Genotype = AC10:
 contrast estimate    SE df t.ratio p.value
 KL - SS    -0.196 0.106 17  -1.849  0.0819

Genotype = AC12:
 contrast estimate    SE df t.ratio p.value
 KL - SS    -0.159 0.106 17  -1.500  0.1519

Genotype = AC8:
 contrast estimate    SE df t.ratio p.value
 KL - SS    -0.198 0.115 17  -1.728  0.1020
##Save p-values

#Genotypes within Sites
Tol_ScoreF_W2_lm.geno<-data.frame(emmeans(Tol_ScoreF_W2_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreF_W2_lm.geno<-Tol_ScoreF_W2_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_W2_lm.geno$group1<-paste(Tol_ScoreF_W2_lm.geno$Site, Tol_ScoreF_W2_lm.geno$group1, sep="_")
Tol_ScoreF_W2_lm.geno$group2<-paste(Tol_ScoreF_W2_lm.geno$Site, Tol_ScoreF_W2_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreF_W2_lm.site<-data.frame(emmeans(Tol_ScoreF_W2_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreF_W2_lm.site<-Tol_ScoreF_W2_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_W2_lm.site$group1<-paste(Tol_ScoreF_W2_lm.site$group1, Tol_ScoreF_W2_lm.site$Genotype, sep="_")
Tol_ScoreF_W2_lm.site$group2<-paste(Tol_ScoreF_W2_lm.site$group2, Tol_ScoreF_W2_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreF_W2_lm.p<-rbind(Tol_ScoreF_W2_lm.geno[,c(1:2,4:8)], Tol_ScoreF_W2_lm.site[,c(1:2,4:8)])
Tol_ScoreF_W2_lm.p<-Tol_ScoreF_W2_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreF_W2_lm.p$Sig<-ifelse(Tol_ScoreF_W2_lm.p$p<0.001, "***", ifelse(Tol_ScoreF_W2_lm.p$p<0.01, "**", ifelse(Tol_ScoreF_W2_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreF_W2_lm.p$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_W2_lm.p))
Tol_ScoreF_W2_lm.p$TimeP<-rep("W2", nrow(Tol_ScoreF_W2_lm.p))

Plot Retention by Site and Genotype

##Summary statistics by Site and Genotype
Tol_ScoreF_W2_SG<-summarySE(TolData_W2, measurevar="Score_Full.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreF_W2_SG.plot<-ggplot(Tol_ScoreF_W2_SG, aes(x=Site.Geno, y=Score_Full.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_Full.prop-se, ymax=Score_Full.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention Full Set")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_ScoreF_W2_lm.p,  y.position=0.8, step.increase=0.2, label="Sig", hide.ns=TRUE); Tol_ScoreF_W2_SG.plot

Color Score Full Set Timepoint M1

Run Model

##Check normality
hist(TolData_M1$Score_Full.prop)

shapiro.test(TolData_M1$Score_Full.prop)

    Shapiro-Wilk normality test

data:  TolData_M1$Score_Full.prop
W = 0.89303, p-value = 0.02162
#Not normal

##Try square transformation
hist((TolData_M1$Score_Full.prop)^2)

shapiro.test((TolData_M1$Score_Full.prop)^2)

    Shapiro-Wilk normality test

data:  (TolData_M1$Score_Full.prop)^2
W = 0.89166, p-value = 0.02032
#Not normal

##Try cubed transformation
hist((TolData_M1$Score_Full.prop)^3)

shapiro.test((TolData_M1$Score_Full.prop)^3)

    Shapiro-Wilk normality test

data:  (TolData_M1$Score_Full.prop)^3
W = 0.87564, p-value = 0.009987
#Not normal

##Model as a function of Site and Genotype
##Model with no transformation and check residuals
Tol_ScoreF_M1_lm<-lm(Score_Full.prop~Site+Genotype+Site:Genotype, data=TolData_M1)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreF_M1_lm)))


#Q-Q plot
qqnorm(resid(Tol_ScoreF_M1_lm)); qqline(resid(Tol_ScoreF_M1_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreF_M1_lm), resid(Tol_ScoreF_M1_lm))

Model Results

Overall

#Model Results
summary(Tol_ScoreF_M1_lm)

Call:
lm(formula = Score_Full.prop ~ Site + Genotype + Site:Genotype, 
    data = TolData_M1)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.273000 -0.037612  0.002175  0.037825  0.271000 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.75883    0.02730  27.794  5.7e-15 ***
Site.L             0.09080    0.03861   2.352 0.031829 *  
Genotype.L        -0.21843    0.04486  -4.869 0.000171 ***
Genotype.Q         0.12173    0.04960   2.454 0.025939 *  
Site.L:Genotype.L  0.10760    0.06344   1.696 0.109255    
Site.L:Genotype.Q  0.01756    0.07014   0.250 0.805486    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1269 on 16 degrees of freedom
Multiple R-squared:  0.7062,    Adjusted R-squared:  0.6144 
F-statistic: 7.693 on 5 and 16 DF,  p-value: 0.0007452
anova(Tol_ScoreF_M1_lm)
Analysis of Variance Table

Response: Score_Full.prop
              Df  Sum Sq  Mean Sq F value    Pr(>F)    
Site           1 0.09331 0.093314  5.7958 0.0284942 *  
Genotype       2 0.47867 0.239337 14.8652 0.0002246 ***
Site:Genotype  2 0.04732 0.023660  1.4695 0.2594726    
Residuals     16 0.25761 0.016100                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Effect Size of Predictors
eta_squared(Tol_ScoreF_M1_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter     | Eta2 |       95% CI
-----------------------------------
Site          | 0.11 | [0.00, 1.00]
Genotype      | 0.55 | [0.21, 1.00]
Site:Genotype | 0.05 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_ScoreF_M1_lm.res<-data.frame(anova(Tol_ScoreF_M1_lm))
Tol_ScoreF_M1_lm.res$Predictor<-rownames(Tol_ScoreF_M1_lm.res)
Tol_ScoreF_M1_lm.res$EtaSq<-c(eta_squared(Tol_ScoreF_M1_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreF_M1_lm.res$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_M1_lm.res))
Tol_ScoreF_M1_lm.res$TimeP<-rep("M1", nrow(Tol_ScoreF_M1_lm.res))
Tol_ScoreF_M1_lm.res<-Tol_ScoreF_M1_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

Significant effect of Site and Genotype. Still checking Site*Genotype for comparability across Timepoints.

Pairwise

#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreF_M1_lm, pairwise~Genotype | Site)
$emmeans
Site = KL:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.948 0.0634 16    0.813    1.082
 AC12      0.605 0.0733 16    0.450    0.761
 AC8       0.531 0.0634 16    0.397    0.665

Site = SS:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.978 0.0634 16    0.844    1.113
 AC12      0.714 0.0733 16    0.558    0.869
 AC8       0.777 0.0634 16    0.643    0.912

Confidence level used: 0.95 

$contrasts
Site = KL:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12   0.3421 0.0969 16   3.530  0.0074
 AC10 - AC8    0.4165 0.0897 16   4.642  0.0007
 AC12 - AC8    0.0744 0.0969 16   0.767  0.7277

Site = SS:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12   0.2650 0.0969 16   2.734  0.0371
 AC10 - AC8    0.2013 0.0897 16   2.244  0.0939
 AC12 - AC8   -0.0636 0.0969 16  -0.657  0.7913

P value adjustment: tukey method for comparing a family of 3 estimates 
#Sites within Genotypes
emmeans(Tol_ScoreF_M1_lm, pairwise~Site | Genotype)
$emmeans
Genotype = AC10:
 Site emmean     SE df lower.CL upper.CL
 KL    0.948 0.0634 16    0.813    1.082
 SS    0.978 0.0634 16    0.844    1.113

Genotype = AC12:
 Site emmean     SE df lower.CL upper.CL
 KL    0.605 0.0733 16    0.450    0.761
 SS    0.714 0.0733 16    0.558    0.869

Genotype = AC8:
 Site emmean     SE df lower.CL upper.CL
 KL    0.531 0.0634 16    0.397    0.665
 SS    0.777 0.0634 16    0.643    0.912

Confidence level used: 0.95 

$contrasts
Genotype = AC10:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.0309 0.0897 16  -0.345  0.7346

Genotype = AC12:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.1081 0.1036 16  -1.044  0.3121

Genotype = AC8:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.2462 0.0897 16  -2.743  0.0144
##Save p-values

#Genotypes within Sites
Tol_ScoreF_M1_lm.geno<-data.frame(emmeans(Tol_ScoreF_M1_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreF_M1_lm.geno<-Tol_ScoreF_M1_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_M1_lm.geno$group1<-paste(Tol_ScoreF_M1_lm.geno$Site, Tol_ScoreF_M1_lm.geno$group1, sep="_")
Tol_ScoreF_M1_lm.geno$group2<-paste(Tol_ScoreF_M1_lm.geno$Site, Tol_ScoreF_M1_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreF_M1_lm.site<-data.frame(emmeans(Tol_ScoreF_M1_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreF_M1_lm.site<-Tol_ScoreF_M1_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_M1_lm.site$group1<-paste(Tol_ScoreF_M1_lm.site$group1, Tol_ScoreF_M1_lm.site$Genotype, sep="_")
Tol_ScoreF_M1_lm.site$group2<-paste(Tol_ScoreF_M1_lm.site$group2, Tol_ScoreF_M1_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreF_M1_lm.p<-rbind(Tol_ScoreF_M1_lm.geno[,c(1:2,4:8)], Tol_ScoreF_M1_lm.site[,c(1:2,4:8)])
Tol_ScoreF_M1_lm.p<-Tol_ScoreF_M1_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreF_M1_lm.p$Sig<-ifelse(Tol_ScoreF_M1_lm.p$p<0.001, "***", ifelse(Tol_ScoreF_M1_lm.p$p<0.01, "**", ifelse(Tol_ScoreF_M1_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreF_M1_lm.p$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_M1_lm.p))
Tol_ScoreF_M1_lm.p$TimeP<-rep("M1", nrow(Tol_ScoreF_M1_lm.p))

Plot Retention by Site and Genotype

##Summary statistics by Site and Genotype
Tol_ScoreF_M1_SG<-summarySE(TolData_M1, measurevar="Score_Full.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreF_M1_SG.plot<-ggplot(Tol_ScoreF_M1_SG, aes(x=Site.Geno, y=Score_Full.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_Full.prop-se, ymax=Score_Full.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention Full Set")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
 ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_ScoreF_M1_lm.p,  y.position=1.1, step.increase=0.2, label="Sig", hide.ns=TRUE); Tol_ScoreF_M1_SG.plot

Color Score Full Set Timepoint M4

Run Model

##Check normality
hist(TolData_M4$Score_Full.prop)

shapiro.test(TolData_M4$Score_Full.prop)

    Shapiro-Wilk normality test

data:  TolData_M4$Score_Full.prop
W = 0.84977, p-value = 0.002165
#Not Normal

##Try square transformation
hist((TolData_M4$Score_Full.prop)^2)

shapiro.test((TolData_M4$Score_Full.prop)^2)

    Shapiro-Wilk normality test

data:  (TolData_M4$Score_Full.prop)^2
W = 0.85122, p-value = 0.002303
#Not Normal

##Try cubed transformation
hist((TolData_M4$Score_Full.prop)^3)

shapiro.test((TolData_M4$Score_Full.prop)^3)

    Shapiro-Wilk normality test

data:  (TolData_M4$Score_Full.prop)^3
W = 0.85118, p-value = 0.002299
#Not Normal

##Model as a function of Site and Genotype
##Model with no transformation and check residuals
Tol_ScoreF_M4_lm<-lm(Score_Full.prop~Site+Genotype+Site:Genotype, data=TolData_M4)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreF_M4_lm)))


#Q-Q plot
qqnorm(resid(Tol_ScoreF_M4_lm)); qqline(resid(Tol_ScoreF_M4_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreF_M4_lm), resid(Tol_ScoreF_M4_lm))

Model Results

Overall

#Model Results
summary(Tol_ScoreF_M4_lm)

Call:
lm(formula = Score_Full.prop ~ Site + Genotype + Site:Genotype, 
    data = TolData_M4)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.18507 -0.02627  0.00710  0.02830  0.13483 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.86734    0.01507  57.567  < 2e-16 ***
Site.L             0.04613    0.02131   2.165   0.0441 *  
Genotype.L        -0.17466    0.02610  -6.693 2.81e-06 ***
Genotype.Q        -0.02464    0.02610  -0.944   0.3575    
Site.L:Genotype.L  0.02404    0.03691   0.651   0.5231    
Site.L:Genotype.Q -0.05278    0.03691  -1.430   0.1698    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.07381 on 18 degrees of freedom
Multiple R-squared:  0.7459,    Adjusted R-squared:  0.6754 
F-statistic: 10.57 on 5 and 18 DF,  p-value: 7.375e-05
anova(Tol_ScoreF_M4_lm)
Analysis of Variance Table

Response: Score_Full.prop
              Df   Sum Sq  Mean Sq F value   Pr(>F)    
Site           1 0.025532 0.025532  4.6865  0.04408 *  
Genotype       2 0.248919 0.124459 22.8449 1.15e-05 ***
Site:Genotype  2 0.013453 0.006726  1.2347  0.31443    
Residuals     18 0.098064 0.005448                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Effect Size of Predictors
eta_squared(Tol_ScoreF_M4_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter     | Eta2 |       95% CI
-----------------------------------
Site          | 0.07 | [0.00, 1.00]
Genotype      | 0.64 | [0.37, 1.00]
Site:Genotype | 0.03 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_ScoreF_M4_lm.res<-data.frame(anova(Tol_ScoreF_M4_lm))
Tol_ScoreF_M4_lm.res$Predictor<-rownames(Tol_ScoreF_M4_lm.res)
Tol_ScoreF_M4_lm.res$EtaSq<-c(eta_squared(Tol_ScoreF_M4_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreF_M4_lm.res$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_M4_lm.res))
Tol_ScoreF_M4_lm.res$TimeP<-rep("M4", nrow(Tol_ScoreF_M4_lm.res))
Tol_ScoreF_M4_lm.res<-Tol_ScoreF_M4_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

Significant effect of Site and Genotype. Still checking Site*Genotype for comparability across Timepoints.

Pairwise

#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreF_M4_lm, pairwise~Genotype | Site)
$emmeans
Site = KL:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.975 0.0369 18    0.898    1.053
 AC12      0.824 0.0369 18    0.747    0.902
 AC8       0.704 0.0369 18    0.627    0.782

Site = SS:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.986 0.0369 18    0.909    1.064
 AC12      0.951 0.0369 18    0.873    1.028
 AC8       0.763 0.0369 18    0.686    0.841

Confidence level used: 0.95 

$contrasts
Site = KL:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12   0.1510 0.0522 18   2.894  0.0249
 AC10 - AC8    0.2711 0.0522 18   5.193  0.0002
 AC12 - AC8    0.1200 0.0522 18   2.299  0.0816

Site = SS:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12   0.0356 0.0522 18   0.682  0.7767
 AC10 - AC8    0.2230 0.0522 18   4.272  0.0013
 AC12 - AC8    0.1874 0.0522 18   3.590  0.0056

P value adjustment: tukey method for comparing a family of 3 estimates 
#Sites within Genotypes
emmeans(Tol_ScoreF_M4_lm, pairwise~Site | Genotype)
$emmeans
Genotype = AC10:
 Site emmean     SE df lower.CL upper.CL
 KL    0.975 0.0369 18    0.898    1.053
 SS    0.986 0.0369 18    0.909    1.064

Genotype = AC12:
 Site emmean     SE df lower.CL upper.CL
 KL    0.824 0.0369 18    0.747    0.902
 SS    0.951 0.0369 18    0.873    1.028

Genotype = AC8:
 Site emmean     SE df lower.CL upper.CL
 KL    0.704 0.0369 18    0.627    0.782
 SS    0.763 0.0369 18    0.686    0.841

Confidence level used: 0.95 

$contrasts
Genotype = AC10:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.0107 0.0522 18  -0.205  0.8395

Genotype = AC12:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.1262 0.0522 18  -2.418  0.0265

Genotype = AC8:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.0588 0.0522 18  -1.127  0.2747
##Save p-values

#Genotypes within Sites
Tol_ScoreF_M4_lm.geno<-data.frame(emmeans(Tol_ScoreF_M4_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreF_M4_lm.geno<-Tol_ScoreF_M4_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_M4_lm.geno$group1<-paste(Tol_ScoreF_M4_lm.geno$Site, Tol_ScoreF_M4_lm.geno$group1, sep="_")
Tol_ScoreF_M4_lm.geno$group2<-paste(Tol_ScoreF_M4_lm.geno$Site, Tol_ScoreF_M4_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreF_M4_lm.site<-data.frame(emmeans(Tol_ScoreF_M4_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreF_M4_lm.site<-Tol_ScoreF_M4_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_M4_lm.site$group1<-paste(Tol_ScoreF_M4_lm.site$group1, Tol_ScoreF_M4_lm.site$Genotype, sep="_")
Tol_ScoreF_M4_lm.site$group2<-paste(Tol_ScoreF_M4_lm.site$group2, Tol_ScoreF_M4_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreF_M4_lm.p<-rbind(Tol_ScoreF_M4_lm.geno[,c(1:2,4:8)], Tol_ScoreF_M4_lm.site[,c(1:2,4:8)])
Tol_ScoreF_M4_lm.p<-Tol_ScoreF_M4_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreF_M4_lm.p$Sig<-ifelse(Tol_ScoreF_M4_lm.p$p<0.001, "***", ifelse(Tol_ScoreF_M4_lm.p$p<0.01, "**", ifelse(Tol_ScoreF_M4_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreF_M4_lm.p$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_M4_lm.p))
Tol_ScoreF_M4_lm.p$TimeP<-rep("M4", nrow(Tol_ScoreF_M4_lm.p))

Plot Retention by Site and Genotype

##Summary statistics by Site and Genotype
Tol_ScoreF_M4_SG<-summarySE(TolData_M4, measurevar="Score_Full.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreF_M4_SG.plot<-ggplot(Tol_ScoreF_M4_SG, aes(x=Site.Geno, y=Score_Full.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_Full.prop-se, ymax=Score_Full.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention Full Set")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_ScoreF_M4_lm.p,  y.position=1.1, step.increase=0.22, label="Sig", hide.ns=TRUE); Tol_ScoreF_M4_SG.plot

Color Score Full Set Timepoint M8

Run Model

##Check normality
hist(TolData_M8$Score_Full.prop)

shapiro.test(TolData_M8$Score_Full.prop)

    Shapiro-Wilk normality test

data:  TolData_M8$Score_Full.prop
W = 0.70466, p-value = 1.161e-05
#Not Normal

##Try square transformation
hist((TolData_M8$Score_Full.prop)^2)

shapiro.test((TolData_M8$Score_Full.prop)^2)

    Shapiro-Wilk normality test

data:  (TolData_M8$Score_Full.prop)^2
W = 0.70835, p-value = 1.301e-05
#Not Normal

##Try cubed transformation
hist((TolData_M8$Score_Full.prop)^3)

shapiro.test((TolData_M8$Score_Full.prop)^3)

    Shapiro-Wilk normality test

data:  (TolData_M8$Score_Full.prop)^3
W = 0.71195, p-value = 1.455e-05
#Not Normal

##Model as a function of Site and Genotype
##Model with no transformation and check residuals
Tol_ScoreF_M8_lm<-lm(Score_Full.prop~Site+Genotype+Site:Genotype, data=TolData_M8)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreF_M8_lm)))


#Q-Q plot
qqnorm(resid(Tol_ScoreF_M8_lm)); qqline(resid(Tol_ScoreF_M8_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreF_M8_lm), resid(Tol_ScoreF_M8_lm))

Model Results

Overall

#Model Results
summary(Tol_ScoreF_M8_lm)

Call:
lm(formula = Score_Full.prop ~ Site + Genotype + Site:Genotype, 
    data = TolData_M8)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.04225 -0.00625  0.00015  0.00675  0.03333 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.982546   0.003520 279.166  < 2e-16 ***
Site.L             0.017943   0.004977   3.605  0.00203 ** 
Genotype.L        -0.014257   0.006096  -2.339  0.03109 *  
Genotype.Q        -0.017213   0.006096  -2.824  0.01125 *  
Site.L:Genotype.L  0.019513   0.008621   2.263  0.03621 *  
Site.L:Genotype.Q  0.016346   0.008621   1.896  0.07412 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.01724 on 18 degrees of freedom
Multiple R-squared:  0.6614,    Adjusted R-squared:  0.5673 
F-statistic: 7.031 on 5 and 18 DF,  p-value: 0.0008379
anova(Tol_ScoreF_M8_lm)
Analysis of Variance Table

Response: Score_Full.prop
              Df    Sum Sq   Mean Sq F value   Pr(>F)   
Site           1 0.0038633 0.0038633 12.9949 0.002025 **
Genotype       2 0.0039963 0.0019982  6.7211 0.006604 **
Site:Genotype  2 0.0025917 0.0012959  4.3589 0.028593 * 
Residuals     18 0.0053513 0.0002973                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Effect Size of Predictors
eta_squared(Tol_ScoreF_M8_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter     | Eta2 |       95% CI
-----------------------------------
Site          | 0.24 | [0.02, 1.00]
Genotype      | 0.25 | [0.00, 1.00]
Site:Genotype | 0.16 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_ScoreF_M8_lm.res<-data.frame(anova(Tol_ScoreF_M8_lm))
Tol_ScoreF_M8_lm.res$Predictor<-rownames(Tol_ScoreF_M8_lm.res)
Tol_ScoreF_M8_lm.res$EtaSq<-c(eta_squared(Tol_ScoreF_M8_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreF_M8_lm.res$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_M8_lm.res))
Tol_ScoreF_M8_lm.res$TimeP<-rep("M8", nrow(Tol_ScoreF_M8_lm.res))
Tol_ScoreF_M8_lm.res<-Tol_ScoreF_M8_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

Significant effect of Site, Genotype, and Site * Genotype.

Pairwise

#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreF_M8_lm, pairwise~Genotype | Site)
$emmeans
Site = KL:
 Genotype emmean      SE df lower.CL upper.CL
 AC10      0.978 0.00862 18    0.960    0.996
 AC12      0.993 0.00862 18    0.975    1.011
 AC8       0.938 0.00862 18    0.920    0.956

Site = SS:
 Genotype emmean      SE df lower.CL upper.CL
 AC10      0.993 0.00862 18    0.975    1.011
 AC12      1.000 0.00862 18    0.982    1.018
 AC8       0.993 0.00862 18    0.974    1.011

Confidence level used: 0.95 

$contrasts
Site = KL:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12 -0.01540 0.0122 18  -1.263  0.4332
 AC10 - AC8   0.03968 0.0122 18   3.254  0.0117
 AC12 - AC8   0.05507 0.0122 18   4.517  0.0007

Site = SS:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12 -0.00660 0.0122 18  -0.541  0.8521
 AC10 - AC8   0.00065 0.0122 18   0.053  0.9984
 AC12 - AC8   0.00725 0.0122 18   0.595  0.8247

P value adjustment: tukey method for comparing a family of 3 estimates 
#Sites within Genotypes
emmeans(Tol_ScoreF_M8_lm, pairwise~Site | Genotype)
$emmeans
Genotype = AC10:
 Site emmean      SE df lower.CL upper.CL
 KL    0.978 0.00862 18    0.960    0.996
 SS    0.993 0.00862 18    0.975    1.011

Genotype = AC12:
 Site emmean      SE df lower.CL upper.CL
 KL    0.993 0.00862 18    0.975    1.011
 SS    1.000 0.00862 18    0.982    1.018

Genotype = AC8:
 Site emmean      SE df lower.CL upper.CL
 KL    0.938 0.00862 18    0.920    0.956
 SS    0.993 0.00862 18    0.974    1.011

Confidence level used: 0.95 

$contrasts
Genotype = AC10:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.0153 0.0122 18  -1.255  0.2256

Genotype = AC12:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.0065 0.0122 18  -0.533  0.6005

Genotype = AC8:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.0543 0.0122 18  -4.456  0.0003
##Save p-values

#Genotypes within Sites
Tol_ScoreF_M8_lm.geno<-data.frame(emmeans(Tol_ScoreF_M8_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreF_M8_lm.geno<-Tol_ScoreF_M8_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_M8_lm.geno$group1<-paste(Tol_ScoreF_M8_lm.geno$Site, Tol_ScoreF_M8_lm.geno$group1, sep="_")
Tol_ScoreF_M8_lm.geno$group2<-paste(Tol_ScoreF_M8_lm.geno$Site, Tol_ScoreF_M8_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreF_M8_lm.site<-data.frame(emmeans(Tol_ScoreF_M8_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreF_M8_lm.site<-Tol_ScoreF_M8_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_M8_lm.site$group1<-paste(Tol_ScoreF_M8_lm.site$group1, Tol_ScoreF_M8_lm.site$Genotype, sep="_")
Tol_ScoreF_M8_lm.site$group2<-paste(Tol_ScoreF_M8_lm.site$group2, Tol_ScoreF_M8_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreF_M8_lm.p<-rbind(Tol_ScoreF_M8_lm.geno[,c(1:2,4:8)], Tol_ScoreF_M8_lm.site[,c(1:2,4:8)])
Tol_ScoreF_M8_lm.p<-Tol_ScoreF_M8_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreF_M8_lm.p$Sig<-ifelse(Tol_ScoreF_M8_lm.p$p<0.001, "***", ifelse(Tol_ScoreF_M8_lm.p$p<0.01, "**", ifelse(Tol_ScoreF_M8_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreF_M8_lm.p$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_M8_lm.p))
Tol_ScoreF_M8_lm.p$TimeP<-rep("M8", nrow(Tol_ScoreF_M8_lm.p))

Plot Retention by Site and Genotype

##Summary statistics by Site and Genotype
Tol_ScoreF_M8_SG<-summarySE(TolData_M8, measurevar="Score_Full.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreF_M8_SG.plot<-ggplot(Tol_ScoreF_M8_SG, aes(x=Site.Geno, y=Score_Full.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_Full.prop-se, ymax=Score_Full.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention Full Set")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_ScoreF_M8_lm.p,  y.position=1.1, step.increase=0.45, label="Sig", hide.ns=TRUE); Tol_ScoreF_M8_SG.plot

Color Score Full Set Timepoint M12

Run Model

##Check normality
hist(TolData_M12$Score_Full.prop)

shapiro.test(TolData_M12$Score_Full.prop)

    Shapiro-Wilk normality test

data:  TolData_M12$Score_Full.prop
W = 0.98901, p-value = 0.9934
#Normal

##Model as a function of Site and Genotype
Tol_ScoreF_M12_lm<-lm(Score_Full.prop~Site+Genotype+Site:Genotype, data=TolData_M12)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreF_M12_lm)))


#Q-Q plot
qqnorm(resid(Tol_ScoreF_M12_lm)); qqline(resid(Tol_ScoreF_M12_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreF_M12_lm), resid(Tol_ScoreF_M12_lm))

Model Results

Overall

#Model Results
summary(Tol_ScoreF_M12_lm)

Call:
lm(formula = Score_Full.prop ~ Site + Genotype + Site:Genotype, 
    data = TolData_M12)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.35442 -0.11678  0.00443  0.10991  0.39832 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.560671   0.043883  12.777 1.83e-10 ***
Site.L             0.004095   0.062059   0.066    0.948    
Genotype.L        -0.006930   0.076007  -0.091    0.928    
Genotype.Q         0.011339   0.076007   0.149    0.883    
Site.L:Genotype.L  0.005825   0.107490   0.054    0.957    
Site.L:Genotype.Q -0.082041   0.107490  -0.763    0.455    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.215 on 18 degrees of freedom
Multiple R-squared:  0.03332,   Adjusted R-squared:  -0.2352 
F-statistic: 0.1241 on 5 and 18 DF,  p-value: 0.9851
anova(Tol_ScoreF_M12_lm)
Analysis of Variance Table

Response: Score_Full.prop
              Df  Sum Sq  Mean Sq F value Pr(>F)
Site           1 0.00020 0.000201  0.0044 0.9481
Genotype       2 0.00141 0.000706  0.0153 0.9848
Site:Genotype  2 0.02706 0.013529  0.2927 0.7497
Residuals     18 0.83190 0.046216               
#Effect Size of Predictors
eta_squared(Tol_ScoreF_M12_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter     |     Eta2 |       95% CI
---------------------------------------
Site          | 2.34e-04 | [0.00, 1.00]
Genotype      | 1.64e-03 | [0.00, 1.00]
Site:Genotype |     0.03 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_ScoreF_M12_lm.res<-data.frame(anova(Tol_ScoreF_M12_lm))
Tol_ScoreF_M12_lm.res$Predictor<-rownames(Tol_ScoreF_M12_lm.res)
Tol_ScoreF_M12_lm.res$EtaSq<-c(eta_squared(Tol_ScoreF_M12_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreF_M12_lm.res$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_M12_lm.res))
Tol_ScoreF_M12_lm.res$TimeP<-rep("M12", nrow(Tol_ScoreF_M12_lm.res))
Tol_ScoreF_M12_lm.res<-Tol_ScoreF_M12_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

No significant effects of Site or Genotype. Still checking Site*Genotype for comparability across Timepoints.

Pairwise

#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreF_M12_lm, pairwise~Genotype | Site)
$emmeans
Site = KL:
 Genotype emmean    SE df lower.CL upper.CL
 AC10      0.594 0.107 18    0.368    0.820
 AC12      0.501 0.107 18    0.275    0.727
 AC8       0.578 0.107 18    0.352    0.804

Site = SS:
 Genotype emmean    SE df lower.CL upper.CL
 AC10      0.546 0.107 18    0.321    0.772
 AC12      0.602 0.107 18    0.376    0.828
 AC8       0.543 0.107 18    0.317    0.768

Confidence level used: 0.95 

$contrasts
Site = KL:
 contrast    estimate    SE df t.ratio p.value
 AC10 - AC12  0.09275 0.152 18   0.610  0.8165
 AC10 - AC8   0.01562 0.152 18   0.103  0.9942
 AC12 - AC8  -0.07712 0.152 18  -0.507  0.8687

Site = SS:
 contrast    estimate    SE df t.ratio p.value
 AC10 - AC12 -0.05518 0.152 18  -0.363  0.9302
 AC10 - AC8   0.00398 0.152 18   0.026  0.9996
 AC12 - AC8   0.05915 0.152 18   0.389  0.9203

P value adjustment: tukey method for comparing a family of 3 estimates 
#Sites within Genotypes
emmeans(Tol_ScoreF_M12_lm, pairwise~Site | Genotype)
$emmeans
Genotype = AC10:
 Site emmean    SE df lower.CL upper.CL
 KL    0.594 0.107 18    0.368    0.820
 SS    0.546 0.107 18    0.321    0.772

Genotype = AC12:
 Site emmean    SE df lower.CL upper.CL
 KL    0.501 0.107 18    0.275    0.727
 SS    0.602 0.107 18    0.376    0.828

Genotype = AC8:
 Site emmean    SE df lower.CL upper.CL
 KL    0.578 0.107 18    0.352    0.804
 SS    0.543 0.107 18    0.317    0.768

Confidence level used: 0.95 

$contrasts
Genotype = AC10:
 contrast estimate    SE df t.ratio p.value
 KL - SS    0.0474 0.152 18   0.312  0.7588

Genotype = AC12:
 contrast estimate    SE df t.ratio p.value
 KL - SS   -0.1005 0.152 18  -0.661  0.5168

Genotype = AC8:
 contrast estimate    SE df t.ratio p.value
 KL - SS    0.0357 0.152 18   0.235  0.8167
##Save p-values

#Genotypes within Sites
Tol_ScoreF_M12_lm.geno<-data.frame(emmeans(Tol_ScoreF_M12_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreF_M12_lm.geno<-Tol_ScoreF_M12_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_M12_lm.geno$group1<-paste(Tol_ScoreF_M12_lm.geno$Site, Tol_ScoreF_M12_lm.geno$group1, sep="_")
Tol_ScoreF_M12_lm.geno$group2<-paste(Tol_ScoreF_M12_lm.geno$Site, Tol_ScoreF_M12_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreF_M12_lm.site<-data.frame(emmeans(Tol_ScoreF_M12_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreF_M12_lm.site<-Tol_ScoreF_M12_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_M12_lm.site$group1<-paste(Tol_ScoreF_M12_lm.site$group1, Tol_ScoreF_M12_lm.site$Genotype, sep="_")
Tol_ScoreF_M12_lm.site$group2<-paste(Tol_ScoreF_M12_lm.site$group2, Tol_ScoreF_M12_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreF_M12_lm.p<-rbind(Tol_ScoreF_M12_lm.geno[,c(1:2,4:8)], Tol_ScoreF_M12_lm.site[,c(1:2,4:8)])
Tol_ScoreF_M12_lm.p<-Tol_ScoreF_M12_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreF_M12_lm.p$Sig<-ifelse(Tol_ScoreF_M12_lm.p$p<0.001, "***", ifelse(Tol_ScoreF_M12_lm.p$p<0.01, "**", ifelse(Tol_ScoreF_M12_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreF_M12_lm.p$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_M12_lm.p))
Tol_ScoreF_M12_lm.p$TimeP<-rep("M12", nrow(Tol_ScoreF_M12_lm.p))

Plot Retention by Site and Genotype

##Summary statistics by Site and Genotype
Tol_ScoreF_M12_SG<-summarySE(TolData_M12, measurevar="Score_Full.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreF_M12_SG.plot<-ggplot(Tol_ScoreF_M12_SG, aes(x=Site.Geno, y=Score_Full.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_Full.prop-se, ymax=Score_Full.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention Full Set")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o); Tol_ScoreF_M12_SG.plot


#+ stat_pvalue_manual(data=Tol_ScoreF_M12_lm.p,  y.position=0.8, step.increase=0.15, label="Sig", hide.ns=TRUE) #No significant differences

Thermal Tolerance Color by Timepoint

Full Set

Run Model

##Check normality
hist(TolData$Score_TP.prop)

shapiro.test(TolData$Score_TP.prop)

    Shapiro-Wilk normality test

data:  TolData$Score_TP.prop
W = 0.90982, p-value = 1.131e-07
#Not Normal

##Try square transformation
hist((TolData$Score_TP.prop)^2)

shapiro.test((TolData$Score_TP.prop)^2)

    Shapiro-Wilk normality test

data:  (TolData$Score_TP.prop)^2
W = 0.91267, p-value = 1.662e-07
#Not normal 

##Try cubed transformation
hist((TolData$Score_TP.prop)^3)

shapiro.test((TolData$Score_TP.prop)^3)

    Shapiro-Wilk normality test

data:  (TolData$Score_TP.prop)^3
W = 0.91228, p-value = 1.576e-07
#Not normal 


##Model as a function of Site and Genotype and Timepoint
##Model with no transformation and check residuals
Tol_ScoreTP_lm<-lm(Score_TP.prop~Site+Genotype+TimeP+ Site:Genotype + Site:TimeP + Genotype:TimeP, data=TolData)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreTP_lm)))


#Q-Q plot
qqnorm(resid(Tol_ScoreTP_lm)); qqline(resid(Tol_ScoreTP_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreTP_lm), resid(Tol_ScoreTP_lm))

Residuals are not great, need to check for other modeling options for Color by Timepoint.

Model Results

#Model Results
summary(Tol_ScoreTP_lm)

Call:
lm(formula = Score_TP.prop ~ Site + Genotype + TimeP + Site:Genotype + 
    Site:TimeP + Genotype:TimeP, data = TolData)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.131312 -0.020263  0.000578  0.024756  0.197254 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         0.881350   0.004721 186.703  < 2e-16 ***
Site.L              0.022025   0.006667   3.303  0.00128 ** 
Genotype.L         -0.069663   0.008087  -8.615 4.55e-14 ***
Genotype.Q         -0.003731   0.008267  -0.451  0.65264    
TimeP.L             0.025048   0.011511   2.176  0.03163 *  
TimeP.Q            -0.087603   0.011572  -7.570 1.05e-11 ***
TimeP.C            -0.118674   0.011539 -10.285  < 2e-16 ***
TimeP^4            -0.042897   0.011556  -3.712  0.00032 ***
TimeP^5            -0.001804   0.011641  -0.155  0.87709    
Site.L:Genotype.L   0.012920   0.011436   1.130  0.26096    
Site.L:Genotype.Q  -0.005427   0.011667  -0.465  0.64272    
Site.L:TimeP.L     -0.042723   0.016279  -2.624  0.00987 ** 
Site.L:TimeP.Q     -0.021374   0.016333  -1.309  0.19329    
Site.L:TimeP.C      0.031161   0.016301   1.912  0.05844 .  
Site.L:TimeP^4     -0.013056   0.016328  -0.800  0.42562    
Site.L:TimeP^5     -0.002359   0.016413  -0.144  0.88595    
Genotype.L:TimeP.L  0.111835   0.019779   5.654 1.18e-07 ***
Genotype.Q:TimeP.L  0.025829   0.020102   1.285  0.20143    
Genotype.L:TimeP.Q  0.033332   0.019692   1.693  0.09325 .  
Genotype.Q:TimeP.Q -0.032889   0.020388  -1.613  0.10947    
Genotype.L:TimeP.C -0.033689   0.019887  -1.694  0.09299 .  
Genotype.Q:TimeP.C  0.020276   0.020079   1.010  0.31472    
Genotype.L:TimeP^4 -0.028548   0.019923  -1.433  0.15463    
Genotype.Q:TimeP^4  0.047390   0.020106   2.357  0.02013 *  
Genotype.L:TimeP^5 -0.005818   0.019758  -0.294  0.76894    
Genotype.Q:TimeP^5 -0.052202   0.020560  -2.539  0.01246 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.05567 on 114 degrees of freedom
Multiple R-squared:  0.7496,    Adjusted R-squared:  0.6947 
F-statistic: 13.65 on 25 and 114 DF,  p-value: < 2.2e-16
anova(Tol_ScoreTP_lm)
Analysis of Variance Table

Response: Score_TP.prop
                Df  Sum Sq  Mean Sq F value    Pr(>F)    
Site             1 0.03614 0.036137 11.6592 0.0008856 ***
Genotype         2 0.22657 0.113283 36.5499 5.438e-13 ***
TimeP            5 0.57600 0.115200 37.1681 < 2.2e-16 ***
Site:Genotype    2 0.00459 0.002295  0.7403 0.4792450    
Site:TimeP       5 0.04293 0.008587  2.7704 0.0212094 *  
Genotype:TimeP  10 0.17174 0.017174  5.5410 1.081e-06 ***
Residuals      114 0.35333 0.003099                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Effect Size of Predictors
eta_squared(Tol_ScoreTP_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter      |     Eta2 |       95% CI
----------------------------------------
Site           |     0.03 | [0.00, 1.00]
Genotype       |     0.16 | [0.06, 1.00]
TimeP          |     0.41 | [0.28, 1.00]
Site:Genotype  | 3.25e-03 | [0.00, 1.00]
Site:TimeP     |     0.03 | [0.00, 1.00]
Genotype:TimeP |     0.12 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_ScoreTP_lm.res<-data.frame(anova(Tol_ScoreTP_lm))
Tol_ScoreTP_lm.res$Predictor<-rownames(Tol_ScoreTP_lm.res)
Tol_ScoreTP_lm.res$EtaSq<-c(eta_squared(Tol_ScoreTP_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreTP_lm.res$Response<-rep("Color_TP", nrow(Tol_ScoreTP_lm.res))
Tol_ScoreTP_lm.res<-Tol_ScoreTP_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

Strong influence of Timepoint, including interactions with variables of interest, Site and Genotype. Will analyze each Timepoint individually.

Color Score by Timepoint Timepoint W1

Run Model

##Check normality
hist(TolData_W1$Score_TP.prop)

shapiro.test(TolData_W1$Score_TP.prop)

    Shapiro-Wilk normality test

data:  TolData_W1$Score_TP.prop
W = 0.95164, p-value = 0.3163
#Normal

##Model as a function of Site and Genotype
Tol_ScoreTP_W1_lm<-lm(Score_TP.prop~Site+Genotype+Site:Genotype, data=TolData_W1)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreTP_W1_lm)))


#Q-Q plot
qqnorm(resid(Tol_ScoreTP_W1_lm)); qqline(resid(Tol_ScoreTP_W1_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreTP_W1_lm), resid(Tol_ScoreTP_W1_lm))

Model Results

Overall

#Model Results
summary(Tol_ScoreTP_W1_lm)

Call:
lm(formula = Score_TP.prop ~ Site + Genotype + Site:Genotype, 
    data = TolData_W1)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.08550 -0.03194  0.00125  0.02194  0.12747 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.85368    0.01204  70.904  < 2e-16 ***
Site.L             0.02035    0.01703   1.195    0.248    
Genotype.L        -0.11079    0.02030  -5.458 4.25e-05 ***
Genotype.Q        -0.02962    0.02140  -1.384    0.184    
Site.L:Genotype.L  0.00375    0.02871   0.131    0.898    
Site.L:Genotype.Q  0.03416    0.03026   1.129    0.275    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.05741 on 17 degrees of freedom
Multiple R-squared:  0.6715,    Adjusted R-squared:  0.5749 
F-statistic:  6.95 on 5 and 17 DF,  p-value: 0.001056
anova(Tol_ScoreTP_W1_lm)
Analysis of Variance Table

Response: Score_TP.prop
              Df   Sum Sq  Mean Sq F value    Pr(>F)    
Site           1 0.004620 0.004620  1.4017 0.2527326    
Genotype       2 0.105653 0.052826 16.0279 0.0001224 ***
Site:Genotype  2 0.004257 0.002129  0.6458 0.5366252    
Residuals     17 0.056030 0.003296                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Effect Size of Predictors
eta_squared(Tol_ScoreTP_W1_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter     | Eta2 |       95% CI
-----------------------------------
Site          | 0.03 | [0.00, 1.00]
Genotype      | 0.62 | [0.32, 1.00]
Site:Genotype | 0.02 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_ScoreTP_W1_lm.res<-data.frame(anova(Tol_ScoreTP_W1_lm))
Tol_ScoreTP_W1_lm.res$Predictor<-rownames(Tol_ScoreTP_W1_lm.res)
Tol_ScoreTP_W1_lm.res$EtaSq<-c(eta_squared(Tol_ScoreTP_W1_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreTP_W1_lm.res$Response<-rep("Color_TP", nrow(Tol_ScoreTP_W1_lm.res))
Tol_ScoreTP_W1_lm.res$TimeP<-rep("W1", nrow(Tol_ScoreTP_W1_lm.res))
Tol_ScoreTP_W1_lm.res<-Tol_ScoreTP_W1_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

Significant effect of Genotype. Still checking Site*Genotype for comparability across Timepoints.

Pairwise

#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreTP_W1_lm, pairwise~Genotype | Site)
$emmeans
Site = KL:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.898 0.0287 17    0.837    0.958
 AC12      0.883 0.0287 17    0.823    0.944
 AC8       0.737 0.0287 17    0.677    0.798

Site = SS:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.942 0.0287 17    0.882    1.003
 AC12      0.873 0.0331 17    0.803    0.942
 AC8       0.789 0.0287 17    0.729    0.850

Confidence level used: 0.95 

$contrasts
Site = KL:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12   0.0143 0.0406 17   0.353  0.9337
 AC10 - AC8    0.1604 0.0406 17   3.952  0.0028
 AC12 - AC8    0.1461 0.0406 17   3.598  0.0060

Site = SS:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12   0.0698 0.0438 17   1.591  0.2763
 AC10 - AC8    0.1529 0.0406 17   3.767  0.0042
 AC12 - AC8    0.0832 0.0438 17   1.897  0.1700

P value adjustment: tukey method for comparing a family of 3 estimates 
#Sites within Genotypes
emmeans(Tol_ScoreTP_W1_lm, pairwise~Site | Genotype)
$emmeans
Genotype = AC10:
 Site emmean     SE df lower.CL upper.CL
 KL    0.898 0.0287 17    0.837    0.958
 SS    0.942 0.0287 17    0.882    1.003

Genotype = AC12:
 Site emmean     SE df lower.CL upper.CL
 KL    0.883 0.0287 17    0.823    0.944
 SS    0.873 0.0331 17    0.803    0.942

Genotype = AC8:
 Site emmean     SE df lower.CL upper.CL
 KL    0.737 0.0287 17    0.677    0.798
 SS    0.789 0.0287 17    0.729    0.850

Confidence level used: 0.95 

$contrasts
Genotype = AC10:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.0447 0.0406 17  -1.102  0.2857

Genotype = AC12:
 contrast estimate     SE df t.ratio p.value
 KL - SS    0.0107 0.0438 17   0.243  0.8107

Genotype = AC8:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.0522 0.0406 17  -1.287  0.2153
##Save p-values

#Genotypes within Sites
Tol_ScoreTP_W1_lm.geno<-data.frame(emmeans(Tol_ScoreTP_W1_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreTP_W1_lm.geno<-Tol_ScoreTP_W1_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_W1_lm.geno$group1<-paste(Tol_ScoreTP_W1_lm.geno$Site, Tol_ScoreTP_W1_lm.geno$group1, sep="_")
Tol_ScoreTP_W1_lm.geno$group2<-paste(Tol_ScoreTP_W1_lm.geno$Site, Tol_ScoreTP_W1_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreTP_W1_lm.site<-data.frame(emmeans(Tol_ScoreTP_W1_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreTP_W1_lm.site<-Tol_ScoreTP_W1_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_W1_lm.site$group1<-paste(Tol_ScoreTP_W1_lm.site$group1, Tol_ScoreTP_W1_lm.site$Genotype, sep="_")
Tol_ScoreTP_W1_lm.site$group2<-paste(Tol_ScoreTP_W1_lm.site$group2, Tol_ScoreTP_W1_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreTP_W1_lm.p<-rbind(Tol_ScoreTP_W1_lm.geno[,c(1:2,4:8)], Tol_ScoreTP_W1_lm.site[,c(1:2,4:8)])
Tol_ScoreTP_W1_lm.p<-Tol_ScoreTP_W1_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreTP_W1_lm.p$Sig<-ifelse(Tol_ScoreTP_W1_lm.p$p<0.001, "***", ifelse(Tol_ScoreTP_W1_lm.p$p<0.01, "**", ifelse(Tol_ScoreTP_W1_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreTP_W1_lm.p$Response<-rep("Color_TP", nrow(Tol_ScoreTP_W1_lm.p))
Tol_ScoreTP_W1_lm.p$TimeP<-rep("W1", nrow(Tol_ScoreTP_W1_lm.p))

Plot Retention by Site and Genotype

##Summary statistics by Site and Genotype
Tol_ScoreTP_W1_SG<-summarySE(TolData_W1, measurevar="Score_TP.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreTP_W1_SG.plot<-ggplot(Tol_ScoreTP_W1_SG, aes(x=Site.Geno, y=Score_TP.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_TP.prop-se, ymax=Score_TP.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention by Timepoint")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_ScoreTP_W1_lm.p,  y.position=1, step.increase=0.45, label="Sig", hide.ns=TRUE); Tol_ScoreTP_W1_SG.plot

Color Score by Timepoint Timepoint W2

Run Model

##Check normality
hist(TolData_W2$Score_TP.prop)

shapiro.test(TolData_W2$Score_TP.prop)

    Shapiro-Wilk normality test

data:  TolData_W2$Score_TP.prop
W = 0.95832, p-value = 0.4302
#Normal

##Model as a function of Site and Genotype
Tol_ScoreTP_W2_lm<-lm(Score_TP.prop~Site+Genotype+Site:Genotype, data=TolData_W2)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreTP_W2_lm)))


#Q-Q plot
qqnorm(resid(Tol_ScoreTP_W2_lm)); qqline(resid(Tol_ScoreTP_W2_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreTP_W2_lm), resid(Tol_ScoreTP_W2_lm))

Model Results

Overall

#Model Results
summary(Tol_ScoreTP_W2_lm)

Call:
lm(formula = Score_TP.prop ~ Site + Genotype + Site:Genotype, 
    data = TolData_W2)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.117075 -0.028442 -0.002075  0.023658  0.099425 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.843522   0.013080  64.488  < 2e-16 ***
Site.L             0.062257   0.018498   3.366  0.00367 ** 
Genotype.L        -0.117129   0.022952  -5.103 8.83e-05 ***
Genotype.Q        -0.042426   0.022356  -1.898  0.07485 .  
Site.L:Genotype.L -0.002071   0.032459  -0.064  0.94987    
Site.L:Genotype.Q  0.004044   0.031616   0.128  0.89972    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.06237 on 17 degrees of freedom
Multiple R-squared:  0.7169,    Adjusted R-squared:  0.6336 
F-statistic:  8.61 on 5 and 17 DF,  p-value: 0.0003242
anova(Tol_ScoreTP_W2_lm)
Analysis of Variance Table

Response: Score_TP.prop
              Df   Sum Sq  Mean Sq F value    Pr(>F)    
Site           1 0.054151 0.054151 13.9200 0.0016623 ** 
Genotype       2 0.113242 0.056621 14.5549 0.0002073 ***
Site:Genotype  2 0.000083 0.000041  0.0106 0.9894534    
Residuals     17 0.066133 0.003890                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Effect Size of Predictors
eta_squared(Tol_ScoreTP_W2_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter     |     Eta2 |       95% CI
---------------------------------------
Site          |     0.23 | [0.01, 1.00]
Genotype      |     0.48 | [0.15, 1.00]
Site:Genotype | 3.53e-04 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_ScoreTP_W2_lm.res<-data.frame(anova(Tol_ScoreTP_W2_lm))
Tol_ScoreTP_W2_lm.res$Predictor<-rownames(Tol_ScoreTP_W2_lm.res)
Tol_ScoreTP_W2_lm.res$EtaSq<-c(eta_squared(Tol_ScoreTP_W2_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreTP_W2_lm.res$Response<-rep("Color_TP", nrow(Tol_ScoreTP_W2_lm.res))
Tol_ScoreTP_W2_lm.res$TimeP<-rep("W2", nrow(Tol_ScoreTP_W2_lm.res))
Tol_ScoreTP_W2_lm.res<-Tol_ScoreTP_W2_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

Significant effect of Site and Genotype. Still checking Site*Genotype for comparability across Timepoints.

Pairwise

#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreTP_W2_lm, pairwise~Genotype | Site)
$emmeans
Site = KL:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.863 0.0312 17    0.797    0.929
 AC12      0.836 0.0312 17    0.771    0.902
 AC8       0.699 0.0312 17    0.633    0.765

Site = SS:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.955 0.0312 17    0.889    1.021
 AC12      0.920 0.0312 17    0.854    0.986
 AC8       0.788 0.0360 17    0.712    0.864

Confidence level used: 0.95 

$contrasts
Site = KL:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12   0.0263 0.0441 17   0.597  0.8236
 AC10 - AC8    0.1636 0.0441 17   3.709  0.0047
 AC12 - AC8    0.1373 0.0441 17   3.112  0.0166

Site = SS:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12   0.0354 0.0441 17   0.803  0.7065
 AC10 - AC8    0.1677 0.0476 17   3.521  0.0070
 AC12 - AC8    0.1323 0.0476 17   2.778  0.0328

P value adjustment: tukey method for comparing a family of 3 estimates 
#Sites within Genotypes
emmeans(Tol_ScoreTP_W2_lm, pairwise~Site | Genotype)
$emmeans
Genotype = AC10:
 Site emmean     SE df lower.CL upper.CL
 KL    0.863 0.0312 17    0.797    0.929
 SS    0.955 0.0312 17    0.889    1.021

Genotype = AC12:
 Site emmean     SE df lower.CL upper.CL
 KL    0.836 0.0312 17    0.771    0.902
 SS    0.920 0.0312 17    0.854    0.986

Genotype = AC8:
 Site emmean     SE df lower.CL upper.CL
 KL    0.699 0.0312 17    0.633    0.765
 SS    0.788 0.0360 17    0.712    0.864

Confidence level used: 0.95 

$contrasts
Genotype = AC10:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.0925 0.0441 17  -2.096  0.0513

Genotype = AC12:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.0834 0.0441 17  -1.890  0.0759

Genotype = AC8:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.0883 0.0476 17  -1.854  0.0812
##Save p-values

#Genotypes within Sites
Tol_ScoreTP_W2_lm.geno<-data.frame(emmeans(Tol_ScoreTP_W2_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreTP_W2_lm.geno<-Tol_ScoreTP_W2_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_W2_lm.geno$group1<-paste(Tol_ScoreTP_W2_lm.geno$Site, Tol_ScoreTP_W2_lm.geno$group1, sep="_")
Tol_ScoreTP_W2_lm.geno$group2<-paste(Tol_ScoreTP_W2_lm.geno$Site, Tol_ScoreTP_W2_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreTP_W2_lm.site<-data.frame(emmeans(Tol_ScoreTP_W2_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreTP_W2_lm.site<-Tol_ScoreTP_W2_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_W2_lm.site$group1<-paste(Tol_ScoreTP_W2_lm.site$group1, Tol_ScoreTP_W2_lm.site$Genotype, sep="_")
Tol_ScoreTP_W2_lm.site$group2<-paste(Tol_ScoreTP_W2_lm.site$group2, Tol_ScoreTP_W2_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreTP_W2_lm.p<-rbind(Tol_ScoreTP_W2_lm.geno[,c(1:2,4:8)], Tol_ScoreTP_W2_lm.site[,c(1:2,4:8)])
Tol_ScoreTP_W2_lm.p<-Tol_ScoreTP_W2_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreTP_W2_lm.p$Sig<-ifelse(Tol_ScoreTP_W2_lm.p$p<0.001, "***", ifelse(Tol_ScoreTP_W2_lm.p$p<0.01, "**", ifelse(Tol_ScoreTP_W2_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreTP_W2_lm.p$Response<-rep("Color_TP", nrow(Tol_ScoreTP_W2_lm.p))
Tol_ScoreTP_W2_lm.p$TimeP<-rep("W2", nrow(Tol_ScoreTP_W2_lm.p))

Plot Retention by Site and Genotype

##Summary statistics by Site and Genotype
Tol_ScoreTP_W2_SG<-summarySE(TolData_W2, measurevar="Score_TP.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreTP_W2_SG.plot<-ggplot(Tol_ScoreTP_W2_SG, aes(x=Site.Geno, y=Score_TP.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_TP.prop-se, ymax=Score_TP.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention by Timepoint")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_ScoreTP_W2_lm.p,  y.position=1, step.increase=0.45, label="Sig", hide.ns=TRUE); Tol_ScoreTP_W2_SG.plot

Color Score by Timepoint Timepoint M1

Run Model

##Check normality
hist(TolData_M1$Score_TP.prop)

shapiro.test(TolData_M1$Score_TP.prop)

    Shapiro-Wilk normality test

data:  TolData_M1$Score_TP.prop
W = 0.9028, p-value = 0.03383
#Not normal

##Try square transformation
hist((TolData_M1$Score_TP.prop)^2)

shapiro.test((TolData_M1$Score_TP.prop)^2)

    Shapiro-Wilk normality test

data:  (TolData_M1$Score_TP.prop)^2
W = 0.90185, p-value = 0.03238
#Not normal

##Try cubed transformation
hist((TolData_M1$Score_TP.prop)^3)

shapiro.test((TolData_M1$Score_TP.prop)^3)

    Shapiro-Wilk normality test

data:  (TolData_M1$Score_TP.prop)^3
W = 0.89818, p-value = 0.02735
#Not normal

##Model as a function of Site and Genotype
##Model with no transformation and check residuals
Tol_ScoreTP_M1_lm<-lm(Score_TP.prop~Site+Genotype+Site:Genotype, data=TolData_M1)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreTP_M1_lm)))


#Q-Q plot
qqnorm(resid(Tol_ScoreTP_M1_lm)); qqline(resid(Tol_ScoreTP_M1_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreTP_M1_lm), resid(Tol_ScoreTP_M1_lm))

Model Results

Overall

#Model Results
summary(Tol_ScoreTP_M1_lm)

Call:
lm(formula = Score_TP.prop ~ Site + Genotype + Site:Genotype, 
    data = TolData_M1)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.125200 -0.020725 -0.001675  0.015662  0.131000 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.866131   0.012681  68.303  < 2e-16 ***
Site.L             0.041582   0.017933   2.319    0.034 *  
Genotype.L        -0.114746   0.020837  -5.507 4.78e-05 ***
Genotype.Q         0.064377   0.023036   2.795    0.013 *  
Site.L:Genotype.L  0.050375   0.029467   1.710    0.107    
Site.L:Genotype.Q  0.004277   0.032577   0.131    0.897    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.05893 on 16 degrees of freedom
Multiple R-squared:  0.7446,    Adjusted R-squared:  0.6647 
F-statistic: 9.327 on 5 and 16 DF,  p-value: 0.0002606
anova(Tol_ScoreTP_M1_lm)
Analysis of Variance Table

Response: Score_TP.prop
              Df   Sum Sq  Mean Sq F value    Pr(>F)    
Site           1 0.019311 0.019311  5.5598   0.03144 *  
Genotype       2 0.132460 0.066230 19.0683 5.822e-05 ***
Site:Genotype  2 0.010210 0.005105  1.4698   0.25940    
Residuals     16 0.055573 0.003473                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Effect Size of Predictors
eta_squared(Tol_ScoreTP_M1_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter     | Eta2 |       95% CI
-----------------------------------
Site          | 0.09 | [0.00, 1.00]
Genotype      | 0.61 | [0.30, 1.00]
Site:Genotype | 0.05 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_ScoreTP_M1_lm.res<-data.frame(anova(Tol_ScoreTP_M1_lm))
Tol_ScoreTP_M1_lm.res$Predictor<-rownames(Tol_ScoreTP_M1_lm.res)
Tol_ScoreTP_M1_lm.res$EtaSq<-c(eta_squared(Tol_ScoreTP_M1_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreTP_M1_lm.res$Response<-rep("Color_TP", nrow(Tol_ScoreTP_M1_lm.res))
Tol_ScoreTP_M1_lm.res$TimeP<-rep("M1", nrow(Tol_ScoreTP_M1_lm.res))
Tol_ScoreTP_M1_lm.res<-Tol_ScoreTP_M1_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

Significant effect of Site and Genotype. Still checking Site*Genotype for comparability across Timepoints.

Pairwise

#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreTP_M1_lm, pairwise~Genotype | Site)
$emmeans
Site = KL:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.968 0.0295 16    0.906    1.031
 AC12      0.787 0.0340 16    0.715    0.859
 AC8       0.755 0.0295 16    0.693    0.818

Site = SS:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.979 0.0295 16    0.917    1.041
 AC12      0.841 0.0340 16    0.768    0.913
 AC8       0.867 0.0295 16    0.805    0.930

Confidence level used: 0.95 

$contrasts
Site = KL:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12   0.1815 0.0450 16   4.032  0.0026
 AC10 - AC8    0.2127 0.0417 16   5.103  0.0003
 AC12 - AC8    0.0312 0.0450 16   0.693  0.7710

Site = SS:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12   0.1385 0.0450 16   3.077  0.0187
 AC10 - AC8    0.1119 0.0417 16   2.685  0.0408
 AC12 - AC8   -0.0266 0.0450 16  -0.591  0.8269

P value adjustment: tukey method for comparing a family of 3 estimates 
#Sites within Genotypes
emmeans(Tol_ScoreTP_M1_lm, pairwise~Site | Genotype)
$emmeans
Genotype = AC10:
 Site emmean     SE df lower.CL upper.CL
 KL    0.968 0.0295 16    0.906    1.031
 SS    0.979 0.0295 16    0.917    1.041

Genotype = AC12:
 Site emmean     SE df lower.CL upper.CL
 KL    0.787 0.0340 16    0.715    0.859
 SS    0.841 0.0340 16    0.768    0.913

Genotype = AC8:
 Site emmean     SE df lower.CL upper.CL
 KL    0.755 0.0295 16    0.693    0.818
 SS    0.867 0.0295 16    0.805    0.930

Confidence level used: 0.95 

$contrasts
Genotype = AC10:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.0109 0.0417 16  -0.262  0.7970

Genotype = AC12:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.0539 0.0481 16  -1.119  0.2795

Genotype = AC8:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.1116 0.0417 16  -2.679  0.0165
##Save p-values

#Genotypes within Sites
Tol_ScoreTP_M1_lm.geno<-data.frame(emmeans(Tol_ScoreTP_M1_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreTP_M1_lm.geno<-Tol_ScoreTP_M1_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_M1_lm.geno$group1<-paste(Tol_ScoreTP_M1_lm.geno$Site, Tol_ScoreTP_M1_lm.geno$group1, sep="_")
Tol_ScoreTP_M1_lm.geno$group2<-paste(Tol_ScoreTP_M1_lm.geno$Site, Tol_ScoreTP_M1_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreTP_M1_lm.site<-data.frame(emmeans(Tol_ScoreTP_M1_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreTP_M1_lm.site<-Tol_ScoreTP_M1_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_M1_lm.site$group1<-paste(Tol_ScoreTP_M1_lm.site$group1, Tol_ScoreTP_M1_lm.site$Genotype, sep="_")
Tol_ScoreTP_M1_lm.site$group2<-paste(Tol_ScoreTP_M1_lm.site$group2, Tol_ScoreTP_M1_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreTP_M1_lm.p<-rbind(Tol_ScoreTP_M1_lm.geno[,c(1:2,4:8)], Tol_ScoreTP_M1_lm.site[,c(1:2,4:8)])
Tol_ScoreTP_M1_lm.p<-Tol_ScoreTP_M1_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreTP_M1_lm.p$Sig<-ifelse(Tol_ScoreTP_M1_lm.p$p<0.001, "***", ifelse(Tol_ScoreTP_M1_lm.p$p<0.01, "**", ifelse(Tol_ScoreTP_M1_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreTP_M1_lm.p$Response<-rep("Color_TP", nrow(Tol_ScoreTP_M1_lm.p))
Tol_ScoreTP_M1_lm.p$TimeP<-rep("M1", nrow(Tol_ScoreTP_M1_lm.p))

Plot Retention by Site and Genotype

##Summary statistics by Site and Genotype
Tol_ScoreTP_M1_SG<-summarySE(TolData_M1, measurevar="Score_TP.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreTP_M1_SG.plot<-ggplot(Tol_ScoreTP_M1_SG, aes(x=Site.Geno, y=Score_TP.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_TP.prop-se, ymax=Score_TP.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention by Timepoint")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
 ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_ScoreTP_M1_lm.p,  y.position=1.05, step.increase=0.3, label="Sig", hide.ns=TRUE); Tol_ScoreTP_M1_SG.plot

Color Score by Timepoint Timepoint M4

Run Model

##Check normality
hist(TolData_M4$Score_TP.prop)

shapiro.test(TolData_M4$Score_TP.prop)

    Shapiro-Wilk normality test

data:  TolData_M4$Score_TP.prop
W = 0.85397, p-value = 0.002593
#Not Normal

##Try square transformation
hist((TolData_M4$Score_TP.prop)^2)

shapiro.test((TolData_M4$Score_TP.prop)^2)

    Shapiro-Wilk normality test

data:  (TolData_M4$Score_TP.prop)^2
W = 0.85439, p-value = 0.00264
#Not Normal

##Try cubed transformation
hist((TolData_M4$Score_TP.prop)^3)

shapiro.test((TolData_M4$Score_TP.prop)^3)

    Shapiro-Wilk normality test

data:  (TolData_M4$Score_TP.prop)^3
W = 0.85457, p-value = 0.002661
#Not Normal

##Model as a function of Site and Genotype
##Model with no transformation and check residuals
Tol_ScoreTP_M4_lm<-lm(Score_TP.prop~Site+Genotype+Site:Genotype, data=TolData_M4)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreTP_M4_lm)))


#Q-Q plot
qqnorm(resid(Tol_ScoreTP_M4_lm)); qqline(resid(Tol_ScoreTP_M4_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreTP_M4_lm), resid(Tol_ScoreTP_M4_lm))

Model Results

Overall

#Model Results
summary(Tol_ScoreTP_M4_lm)

Call:
lm(formula = Score_TP.prop ~ Site + Genotype + Site:Genotype, 
    data = TolData_M4)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.070400 -0.009438  0.004100  0.011281  0.057100 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.940608   0.006243 150.654  < 2e-16 ***
Site.L             0.010536   0.008830   1.193   0.2483    
Genotype.L        -0.075254   0.010814  -6.959 1.68e-06 ***
Genotype.Q        -0.007308   0.010814  -0.676   0.5078    
Site.L:Genotype.L -0.006950   0.015293  -0.454   0.6549    
Site.L:Genotype.Q -0.032086   0.015293  -2.098   0.0503 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.03059 on 18 degrees of freedom
Multiple R-squared:  0.7531,    Adjusted R-squared:  0.6846 
F-statistic: 10.98 on 5 and 18 DF,  p-value: 5.763e-05
anova(Tol_ScoreTP_M4_lm)
Analysis of Variance Table

Response: Score_TP.prop
              Df   Sum Sq   Mean Sq F value    Pr(>F)    
Site           1 0.001332 0.0013321  1.4238    0.2483    
Genotype       2 0.045732 0.0228662 24.4414 7.407e-06 ***
Site:Genotype  2 0.004311 0.0021557  2.3042    0.1285    
Residuals     18 0.016840 0.0009355                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#Effect Size of Predictors
eta_squared(Tol_ScoreTP_M4_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter     | Eta2 |       95% CI
-----------------------------------
Site          | 0.02 | [0.00, 1.00]
Genotype      | 0.67 | [0.41, 1.00]
Site:Genotype | 0.06 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_ScoreTP_M4_lm.res<-data.frame(anova(Tol_ScoreTP_M4_lm))
Tol_ScoreTP_M4_lm.res$Predictor<-rownames(Tol_ScoreTP_M4_lm.res)
Tol_ScoreTP_M4_lm.res$EtaSq<-c(eta_squared(Tol_ScoreTP_M4_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreTP_M4_lm.res$Response<-rep("Color_TP", nrow(Tol_ScoreTP_M4_lm.res))
Tol_ScoreTP_M4_lm.res$TimeP<-rep("M4", nrow(Tol_ScoreTP_M4_lm.res))
Tol_ScoreTP_M4_lm.res<-Tol_ScoreTP_M4_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

Significant effect of Genotype. Still checking Site*Genotype for comparability across Timepoints.

Pairwise

#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreTP_M4_lm, pairwise~Genotype | Site)
$emmeans
Site = KL:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.989 0.0153 18    0.957    1.021
 AC12      0.921 0.0153 18    0.888    0.953
 AC8       0.890 0.0153 18    0.858    0.922

Site = SS:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.993 0.0153 18    0.960    1.025
 AC12      0.973 0.0153 18    0.940    1.005
 AC8       0.879 0.0153 18    0.847    0.911

Confidence level used: 0.95 

$contrasts
Site = KL:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12   0.0686 0.0216 18   3.171  0.0139
 AC10 - AC8    0.0995 0.0216 18   4.599  0.0006
 AC12 - AC8    0.0309 0.0216 18   1.429  0.3478

Site = SS:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12   0.0199 0.0216 18   0.922  0.6335
 AC10 - AC8    0.1134 0.0216 18   5.242  0.0002
 AC12 - AC8    0.0934 0.0216 18   4.320  0.0011

P value adjustment: tukey method for comparing a family of 3 estimates 
#Sites within Genotypes
emmeans(Tol_ScoreTP_M4_lm, pairwise~Site | Genotype)
$emmeans
Genotype = AC10:
 Site emmean     SE df lower.CL upper.CL
 KL    0.989 0.0153 18    0.957    1.021
 SS    0.993 0.0153 18    0.960    1.025

Genotype = AC12:
 Site emmean     SE df lower.CL upper.CL
 KL    0.921 0.0153 18    0.888    0.953
 SS    0.973 0.0153 18    0.940    1.005

Genotype = AC8:
 Site emmean     SE df lower.CL upper.CL
 KL    0.890 0.0153 18    0.858    0.922
 SS    0.879 0.0153 18    0.847    0.911

Confidence level used: 0.95 

$contrasts
Genotype = AC10:
 contrast estimate     SE df t.ratio p.value
 KL - SS  -0.00332 0.0216 18  -0.154  0.8795

Genotype = AC12:
 contrast estimate     SE df t.ratio p.value
 KL - SS  -0.05195 0.0216 18  -2.402  0.0273

Genotype = AC8:
 contrast estimate     SE df t.ratio p.value
 KL - SS   0.01057 0.0216 18   0.489  0.6308
##Save p-values

#Genotypes within Sites
Tol_ScoreTP_M4_lm.geno<-data.frame(emmeans(Tol_ScoreTP_M4_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreTP_M4_lm.geno<-Tol_ScoreTP_M4_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_M4_lm.geno$group1<-paste(Tol_ScoreTP_M4_lm.geno$Site, Tol_ScoreTP_M4_lm.geno$group1, sep="_")
Tol_ScoreTP_M4_lm.geno$group2<-paste(Tol_ScoreTP_M4_lm.geno$Site, Tol_ScoreTP_M4_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreTP_M4_lm.site<-data.frame(emmeans(Tol_ScoreTP_M4_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreTP_M4_lm.site<-Tol_ScoreTP_M4_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_M4_lm.site$group1<-paste(Tol_ScoreTP_M4_lm.site$group1, Tol_ScoreTP_M4_lm.site$Genotype, sep="_")
Tol_ScoreTP_M4_lm.site$group2<-paste(Tol_ScoreTP_M4_lm.site$group2, Tol_ScoreTP_M4_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreTP_M4_lm.p<-rbind(Tol_ScoreTP_M4_lm.geno[,c(1:2,4:8)], Tol_ScoreTP_M4_lm.site[,c(1:2,4:8)])
Tol_ScoreTP_M4_lm.p<-Tol_ScoreTP_M4_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreTP_M4_lm.p$Sig<-ifelse(Tol_ScoreTP_M4_lm.p$p<0.001, "***", ifelse(Tol_ScoreTP_M4_lm.p$p<0.01, "**", ifelse(Tol_ScoreTP_M4_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreTP_M4_lm.p$Response<-rep("Color_TP", nrow(Tol_ScoreTP_M4_lm.p))
Tol_ScoreTP_M4_lm.p$TimeP<-rep("M4", nrow(Tol_ScoreTP_M4_lm.p))

Plot Retention by Site and Genotype

##Summary statistics by Site and Genotype
Tol_ScoreTP_M4_SG<-summarySE(TolData_M4, measurevar="Score_TP.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreTP_M4_SG.plot<-ggplot(Tol_ScoreTP_M4_SG, aes(x=Site.Geno, y=Score_TP.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_TP.prop-se, ymax=Score_TP.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention by Timepoint")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_ScoreTP_M4_lm.p,  y.position=1.1, step.increase=0.4, label="Sig", hide.ns=TRUE); Tol_ScoreTP_M4_SG.plot

Color Score by Timepoint Timepoint M8

Run Model

##Check normality
hist(TolData_M8$Score_TP.prop)

shapiro.test(TolData_M8$Score_TP.prop)

    Shapiro-Wilk normality test

data:  TolData_M8$Score_TP.prop
W = 0.76126, p-value = 7.305e-05
#Not Normal

##Try square transformation
hist((TolData_M8$Score_TP.prop)^2)

shapiro.test((TolData_M8$Score_TP.prop)^2)

    Shapiro-Wilk normality test

data:  (TolData_M8$Score_TP.prop)^2
W = 0.76269, p-value = 7.676e-05
#Not Normal

##Try cubed transformation
hist((TolData_M8$Score_TP.prop)^3)

shapiro.test((TolData_M8$Score_TP.prop)^3)

    Shapiro-Wilk normality test

data:  (TolData_M8$Score_TP.prop)^3
W = 0.764, p-value = 8.031e-05
#Not Normal

##Model as a function of Site and Genotype
##Model with no transformation and check residuals
Tol_ScoreTP_M8_lm<-lm(Score_TP.prop~Site+Genotype+Site:Genotype, data=TolData_M8)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreTP_M8_lm)))


#Q-Q plot
qqnorm(resid(Tol_ScoreTP_M8_lm)); qqline(resid(Tol_ScoreTP_M8_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreTP_M8_lm), resid(Tol_ScoreTP_M8_lm))

Model Results

Overall

#Model Results
summary(Tol_ScoreTP_M8_lm)

Call:
lm(formula = Score_TP.prop ~ Site + Genotype + Site:Genotype, 
    data = TolData_M8)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.033825 -0.003287  0.003600  0.007875  0.028675 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.9866958  0.0034800 283.534   <2e-16 ***
Site.L             0.0009251  0.0049214   0.188   0.8530    
Genotype.L         0.0023953  0.0060275   0.397   0.6957    
Genotype.Q        -0.0118851  0.0060275  -1.972   0.0642 .  
Site.L:Genotype.L  0.0174125  0.0085242   2.043   0.0560 .  
Site.L:Genotype.Q -0.0026775  0.0085242  -0.314   0.7571    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.01705 on 18 degrees of freedom
Multiple R-squared:  0.317, Adjusted R-squared:  0.1272 
F-statistic: 1.671 on 5 and 18 DF,  p-value: 0.1927
anova(Tol_ScoreTP_M8_lm)
Analysis of Variance Table

Response: Score_TP.prop
              Df    Sum Sq    Mean Sq F value Pr(>F)
Site           1 0.0000103 0.00001027  0.0353 0.8530
Genotype       2 0.0011760 0.00058798  2.0230 0.1612
Site:Genotype  2 0.0012415 0.00062073  2.1357 0.1471
Residuals     18 0.0052317 0.00029065               
#Effect Size of Predictors
eta_squared(Tol_ScoreTP_M8_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter     |     Eta2 |       95% CI
---------------------------------------
Site          | 1.34e-03 | [0.00, 1.00]
Genotype      |     0.15 | [0.00, 1.00]
Site:Genotype |     0.16 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_ScoreTP_M8_lm.res<-data.frame(anova(Tol_ScoreTP_M8_lm))
Tol_ScoreTP_M8_lm.res$Predictor<-rownames(Tol_ScoreTP_M8_lm.res)
Tol_ScoreTP_M8_lm.res$EtaSq<-c(eta_squared(Tol_ScoreTP_M8_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreTP_M8_lm.res$Response<-rep("Color_TP", nrow(Tol_ScoreTP_M8_lm.res))
Tol_ScoreTP_M8_lm.res$TimeP<-rep("M8", nrow(Tol_ScoreTP_M8_lm.res))
Tol_ScoreTP_M8_lm.res<-Tol_ScoreTP_M8_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

No significant effects of Site or Genotype. Still checking Site*Genotype for comparability across Timepoints.

Pairwise

#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreTP_M8_lm, pairwise~Genotype | Site)
$emmeans
Site = KL:
 Genotype emmean      SE df lower.CL upper.CL
 AC10      0.989 0.00852 18    0.971    1.007
 AC12      0.994 0.00852 18    0.976    1.012
 AC8       0.975 0.00852 18    0.957    0.993

Site = SS:
 Genotype emmean      SE df lower.CL upper.CL
 AC10      0.971 0.00852 18    0.953    0.989
 AC12      0.999 0.00852 18    0.981    1.017
 AC8       0.992 0.00852 18    0.974    1.010

Confidence level used: 0.95 

$contrasts
Site = KL:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12 -0.00522 0.0121 18  -0.433  0.9022
 AC10 - AC8   0.01402 0.0121 18   1.163  0.4892
 AC12 - AC8   0.01925 0.0121 18   1.597  0.2725

Site = SS:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12 -0.02728 0.0121 18  -2.263  0.0875
 AC10 - AC8  -0.02080 0.0121 18  -1.725  0.2232
 AC12 - AC8   0.00647 0.0121 18   0.537  0.8542

P value adjustment: tukey method for comparing a family of 3 estimates 
#Sites within Genotypes
emmeans(Tol_ScoreTP_M8_lm, pairwise~Site | Genotype)
$emmeans
Genotype = AC10:
 Site emmean      SE df lower.CL upper.CL
 KL    0.989 0.00852 18    0.971    1.007
 SS    0.971 0.00852 18    0.953    0.989

Genotype = AC12:
 Site emmean      SE df lower.CL upper.CL
 KL    0.994 0.00852 18    0.976    1.012
 SS    0.999 0.00852 18    0.981    1.017

Genotype = AC8:
 Site emmean      SE df lower.CL upper.CL
 KL    0.975 0.00852 18    0.957    0.993
 SS    0.992 0.00852 18    0.974    1.010

Confidence level used: 0.95 

$contrasts
Genotype = AC10:
 contrast estimate     SE df t.ratio p.value
 KL - SS    0.0176 0.0121 18   1.464  0.1604

Genotype = AC12:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.0044 0.0121 18  -0.365  0.7194

Genotype = AC8:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.0172 0.0121 18  -1.425  0.1713
##Save p-values

#Genotypes within Sites
Tol_ScoreTP_M8_lm.geno<-data.frame(emmeans(Tol_ScoreTP_M8_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreTP_M8_lm.geno<-Tol_ScoreTP_M8_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_M8_lm.geno$group1<-paste(Tol_ScoreTP_M8_lm.geno$Site, Tol_ScoreTP_M8_lm.geno$group1, sep="_")
Tol_ScoreTP_M8_lm.geno$group2<-paste(Tol_ScoreTP_M8_lm.geno$Site, Tol_ScoreTP_M8_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreTP_M8_lm.site<-data.frame(emmeans(Tol_ScoreTP_M8_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreTP_M8_lm.site<-Tol_ScoreTP_M8_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_M8_lm.site$group1<-paste(Tol_ScoreTP_M8_lm.site$group1, Tol_ScoreTP_M8_lm.site$Genotype, sep="_")
Tol_ScoreTP_M8_lm.site$group2<-paste(Tol_ScoreTP_M8_lm.site$group2, Tol_ScoreTP_M8_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreTP_M8_lm.p<-rbind(Tol_ScoreTP_M8_lm.geno[,c(1:2,4:8)], Tol_ScoreTP_M8_lm.site[,c(1:2,4:8)])
Tol_ScoreTP_M8_lm.p<-Tol_ScoreTP_M8_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreTP_M8_lm.p$Sig<-ifelse(Tol_ScoreTP_M8_lm.p$p<0.001, "***", ifelse(Tol_ScoreTP_M8_lm.p$p<0.01, "**", ifelse(Tol_ScoreTP_M8_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreTP_M8_lm.p$Response<-rep("Color_TP", nrow(Tol_ScoreTP_M8_lm.p))
Tol_ScoreTP_M8_lm.p$TimeP<-rep("M8", nrow(Tol_ScoreTP_M8_lm.p))

Plot Retention by Site and Genotype

##Summary statistics by Site and Genotype
Tol_ScoreTP_M8_SG<-summarySE(TolData_M8, measurevar="Score_TP.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreTP_M8_SG.plot<-ggplot(Tol_ScoreTP_M8_SG, aes(x=Site.Geno, y=Score_TP.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_TP.prop-se, ymax=Score_TP.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention by Timepoint")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o); Tol_ScoreTP_M8_SG.plot


#+ stat_pvalue_manual(data=Tol_ScoreTP_M8_lm.p,  y.position=0.65, step.increase=0.25, label="Sig", hide.ns=TRUE) #No significant differences

Color Score by Timepoint Timepoint M12

Run Model

##Check normality
hist(TolData_M12$Score_TP.prop)

shapiro.test(TolData_M12$Score_TP.prop)

    Shapiro-Wilk normality test

data:  TolData_M12$Score_TP.prop
W = 0.96756, p-value = 0.6074
#Normal

##Model as a function of Site and Genotype
Tol_ScoreTP_M12_lm<-lm(Score_TP.prop~Site+Genotype+Site:Genotype, data=TolData_M12)

Check Residuals

##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreTP_M12_lm)))


#Q-Q plot
qqnorm(resid(Tol_ScoreTP_M12_lm)); qqline(resid(Tol_ScoreTP_M12_lm))


##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreTP_M12_lm), resid(Tol_ScoreTP_M12_lm))

Model Results

Overall

#Model Results
summary(Tol_ScoreTP_M12_lm)

Call:
lm(formula = Score_TP.prop ~ Site + Genotype + Site:Genotype, 
    data = TolData_M12)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.117625 -0.052444 -0.007313  0.045694  0.180200 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.796079   0.017504  45.479   <2e-16 ***
Site.L            -0.006170   0.024755  -0.249    0.806    
Genotype.L        -0.002961   0.030319  -0.098    0.923    
Genotype.Q         0.006986   0.030319   0.230    0.820    
Site.L:Genotype.L  0.014288   0.042877   0.333    0.743    
Site.L:Genotype.Q -0.034966   0.042877  -0.815    0.425    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.08575 on 18 degrees of freedom
Multiple R-squared:  0.04766,   Adjusted R-squared:  -0.2169 
F-statistic: 0.1802 on 5 and 18 DF,  p-value: 0.9665
anova(Tol_ScoreTP_M12_lm)
Analysis of Variance Table

Response: Score_TP.prop
              Df   Sum Sq   Mean Sq F value Pr(>F)
Site           1 0.000457 0.0004568  0.0621 0.8060
Genotype       2 0.000461 0.0002303  0.0313 0.9692
Site:Genotype  2 0.005707 0.0028535  0.3880 0.6839
Residuals     18 0.132367 0.0073537               
#Effect Size of Predictors
eta_squared(Tol_ScoreTP_M12_lm, partial=FALSE)
# Effect Size for ANOVA (Type I)

Parameter     |     Eta2 |       95% CI
---------------------------------------
Site          | 3.29e-03 | [0.00, 1.00]
Genotype      | 3.31e-03 | [0.00, 1.00]
Site:Genotype |     0.04 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
##Save model results
Tol_ScoreTP_M12_lm.res<-data.frame(anova(Tol_ScoreTP_M12_lm))
Tol_ScoreTP_M12_lm.res$Predictor<-rownames(Tol_ScoreTP_M12_lm.res)
Tol_ScoreTP_M12_lm.res$EtaSq<-c(eta_squared(Tol_ScoreTP_M12_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreTP_M12_lm.res$Response<-rep("Color_TP", nrow(Tol_ScoreTP_M12_lm.res))
Tol_ScoreTP_M12_lm.res$TimeP<-rep("M12", nrow(Tol_ScoreTP_M12_lm.res))
Tol_ScoreTP_M12_lm.res<-Tol_ScoreTP_M12_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

No significant effects of Site or Genotype. Still checking Site*Genotype for comparability across Timepoints.

Pairwise

#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreTP_M12_lm, pairwise~Genotype | Site)
$emmeans
Site = KL:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.823 0.0429 18    0.733    0.913
 AC12      0.775 0.0429 18    0.684    0.865
 AC8       0.804 0.0429 18    0.714    0.894

Site = SS:
 Genotype emmean     SE df lower.CL upper.CL
 AC10      0.779 0.0429 18    0.689    0.870
 AC12      0.806 0.0429 18    0.716    0.896
 AC8       0.790 0.0429 18    0.699    0.880

Confidence level used: 0.95 

$contrasts
Site = KL:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12   0.0481 0.0606 18   0.793  0.7121
 AC10 - AC8    0.0185 0.0606 18   0.305  0.9503
 AC12 - AC8   -0.0296 0.0606 18  -0.488  0.8778

Site = SS:
 contrast    estimate     SE df t.ratio p.value
 AC10 - AC12  -0.0268 0.0606 18  -0.442  0.8987
 AC10 - AC8   -0.0101 0.0606 18  -0.167  0.9848
 AC12 - AC8    0.0167 0.0606 18   0.275  0.9593

P value adjustment: tukey method for comparing a family of 3 estimates 
#Sites within Genotypes
emmeans(Tol_ScoreTP_M12_lm, pairwise~Site | Genotype)
$emmeans
Genotype = AC10:
 Site emmean     SE df lower.CL upper.CL
 KL    0.823 0.0429 18    0.733    0.913
 SS    0.779 0.0429 18    0.689    0.870

Genotype = AC12:
 Site emmean     SE df lower.CL upper.CL
 KL    0.775 0.0429 18    0.684    0.865
 SS    0.806 0.0429 18    0.716    0.896

Genotype = AC8:
 Site emmean     SE df lower.CL upper.CL
 KL    0.804 0.0429 18    0.714    0.894
 SS    0.790 0.0429 18    0.699    0.880

Confidence level used: 0.95 

$contrasts
Genotype = AC10:
 contrast estimate     SE df t.ratio p.value
 KL - SS    0.0432 0.0606 18   0.712  0.4853

Genotype = AC12:
 contrast estimate     SE df t.ratio p.value
 KL - SS   -0.0316 0.0606 18  -0.522  0.6081

Genotype = AC8:
 contrast estimate     SE df t.ratio p.value
 KL - SS    0.0146 0.0606 18   0.241  0.8121
##Save p-values

#Genotypes within Sites
Tol_ScoreTP_M12_lm.geno<-data.frame(emmeans(Tol_ScoreTP_M12_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreTP_M12_lm.geno<-Tol_ScoreTP_M12_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_M12_lm.geno$group1<-paste(Tol_ScoreTP_M12_lm.geno$Site, Tol_ScoreTP_M12_lm.geno$group1, sep="_")
Tol_ScoreTP_M12_lm.geno$group2<-paste(Tol_ScoreTP_M12_lm.geno$Site, Tol_ScoreTP_M12_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreTP_M12_lm.site<-data.frame(emmeans(Tol_ScoreTP_M12_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreTP_M12_lm.site<-Tol_ScoreTP_M12_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_M12_lm.site$group1<-paste(Tol_ScoreTP_M12_lm.site$group1, Tol_ScoreTP_M12_lm.site$Genotype, sep="_")
Tol_ScoreTP_M12_lm.site$group2<-paste(Tol_ScoreTP_M12_lm.site$group2, Tol_ScoreTP_M12_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreTP_M12_lm.p<-rbind(Tol_ScoreTP_M12_lm.geno[,c(1:2,4:8)], Tol_ScoreTP_M12_lm.site[,c(1:2,4:8)])
Tol_ScoreTP_M12_lm.p<-Tol_ScoreTP_M12_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreTP_M12_lm.p$Sig<-ifelse(Tol_ScoreTP_M12_lm.p$p<0.001, "***", ifelse(Tol_ScoreTP_M12_lm.p$p<0.01, "**", ifelse(Tol_ScoreTP_M12_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreTP_M12_lm.p$Response<-rep("Color_TP", nrow(Tol_ScoreTP_M12_lm.p))
Tol_ScoreTP_M12_lm.p$TimeP<-rep("M12", nrow(Tol_ScoreTP_M12_lm.p))

Plot Retention by Site and Genotype

##Summary statistics by Site and Genotype
Tol_ScoreTP_M12_SG<-summarySE(TolData_M12, measurevar="Score_TP.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreTP_M12_SG.plot<-ggplot(Tol_ScoreTP_M12_SG, aes(x=Site.Geno, y=Score_TP.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_TP.prop-se, ymax=Score_TP.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention by Timepoint")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o); Tol_ScoreTP_M12_SG.plot


#+ stat_pvalue_manual(data=Tol_ScoreTP_M12_lm.p,  y.position=0.8, step.increase=0.15, label="Sig", hide.ns=TRUE) #No significant differences

Contrasts Across Metrics

Combine Contrast Results

Creating a heatmap to compare the direction and significance of pairwise comparisons across Tolerance metrics. Positive estimates indicate that Group 1 > Group 2. P values of less than 0.05 are considered significant.

##Combine Results
Tol_contrasts<-rbind(Tol_Chl_W1_lm.p, Tol_Chl_W2_lm.p, Tol_Chl_M1_lm.p, 
                     Tol_Chl_M4_lm.p, Tol_Chl_M8_lm.p, Tol_Chl_M12_lm.p,
                     Tol_Sym_W2_lm.p, Tol_Sym_M1_lm.p, 
                     Tol_Sym_M4_lm.p, Tol_Sym_M8_lm.p, Tol_Sym_M12_lm.p,
                     Tol_ScoreF_W1_lm.p, Tol_ScoreF_W2_lm.p,
                     Tol_ScoreF_M1_lm.p, Tol_ScoreF_M4_lm.p, 
                     Tol_ScoreF_M8_lm.p, Tol_ScoreF_M12_lm.p,
                     Tol_ScoreTP_W1_lm.p, Tol_ScoreTP_W2_lm.p,
                     Tol_ScoreTP_M1_lm.p, Tol_ScoreTP_M4_lm.p, 
                     Tol_ScoreTP_M8_lm.p, Tol_ScoreTP_M12_lm.p)

##Create Contrast Column
Tol_contrasts$Contrast<-paste(Tol_contrasts$group1, Tol_contrasts$group2, sep=" v. ")

Standardize Response Scale

Convert Estimate to the Response scale (instead of log +1 scale) for models where the response was log transformed

Tol_contrasts$Estimate<-Tol_contrasts$estimate

#Chl W1
Tol_contrasts$Estimate[which(Tol_contrasts$Response=="Chlorophyll" & Tol_contrasts$TimeP=="W1")]<-c(exp(Tol_contrasts$estimate[which(Tol_contrasts$Response=="Chlorophyll" & Tol_contrasts$TimeP=="W1")])-1)

#Chl M4
Tol_contrasts$Estimate[which(Tol_contrasts$Response=="Chlorophyll" & Tol_contrasts$TimeP=="M4")]<-c(exp(Tol_contrasts$estimate[which(Tol_contrasts$Response=="Chlorophyll" & Tol_contrasts$TimeP=="M4")])-1)

#Chl M12
Tol_contrasts$Estimate[which(Tol_contrasts$Response=="Chlorophyll" & Tol_contrasts$TimeP=="M12")]<-c(exp(Tol_contrasts$estimate[which(Tol_contrasts$Response=="Chlorophyll" & Tol_contrasts$TimeP=="M12")])-1)

#Sym M1
Tol_contrasts$Estimate[which(Tol_contrasts$Response=="Symbionts" & Tol_contrasts$TimeP=="M1")]<-c(exp(Tol_contrasts$estimate[which(Tol_contrasts$Response=="Symbionts" & Tol_contrasts$TimeP=="M1")])-1)

Contrast Heatmaps

Week 1

Tol_Pairs_W1.plot<-ggplot(data=Tol_contrasts[which(Tol_contrasts$TimeP=="W1"),], aes(x=Response, y=Contrast, fill=Estimate))+
  geom_tile()+
  geom_text(aes(label=Sig), size=sig.sz)+
  scale_fill_gradient2(low="#BA1E02FF", mid="white", high="#3BA0FDFF")+
  theme_bw()+
  ggtitle("Pairwise Contrasts Week 1")+
  theme(axis.text.x=element_text(size=axis.title.sz, colour="black", angle=45, hjust=1),
        axis.text.y=element_text(size=axis.txt.sz-1, colour="black"),
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="", fill="Estimate");Tol_Pairs_W1.plot

Week 2

Tol_Pairs_W2.plot<-ggplot(data=Tol_contrasts[which(Tol_contrasts$TimeP=="W2"),], aes(x=Response, y=Contrast, fill=Estimate))+
  geom_tile()+
  geom_text(aes(label=Sig), size=sig.sz)+
  scale_fill_gradient2(low="#BA1E02FF", mid="white", high="#3BA0FDFF")+
  theme_bw()+
  ggtitle("Pairwise Contrasts Week 2")+
  theme(axis.text.x=element_text(size=axis.title.sz, colour="black", angle=45, hjust=1),
        axis.text.y=element_text(size=axis.txt.sz-1, colour="black"),
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="", fill="Estimate");Tol_Pairs_W2.plot

Month 1

Tol_Pairs_M1.plot<-ggplot(data=Tol_contrasts[which(Tol_contrasts$TimeP=="M1"),], aes(x=Response, y=Contrast, fill=Estimate))+
  geom_tile()+
  geom_text(aes(label=Sig), size=sig.sz)+
  scale_fill_gradient2(low="#BA1E02FF", mid="white", high="#3BA0FDFF")+
  theme_bw()+
  ggtitle("Pairwise Contrasts Month 1")+
  theme(axis.text.x=element_text(size=axis.title.sz, colour="black", angle=45, hjust=1),
        axis.text.y=element_text(size=axis.txt.sz-1, colour="black"),
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="", fill="Estimate");Tol_Pairs_M1.plot

Month 4

Tol_Pairs_M4.plot<-ggplot(data=Tol_contrasts[which(Tol_contrasts$TimeP=="M4"),], aes(x=Response, y=Contrast, fill=Estimate))+
  geom_tile()+
  geom_text(aes(label=Sig), size=sig.sz)+
  scale_fill_gradient2(low="#BA1E02FF", mid="white", high="#3BA0FDFF")+
  theme_bw()+
  ggtitle("Pairwise Contrasts Month 4")+
  theme(axis.text.x=element_text(size=axis.title.sz, colour="black", angle=45, hjust=1),
        axis.text.y=element_text(size=axis.txt.sz-1, colour="black"),
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="", fill="Estimate");Tol_Pairs_M4.plot

Month 8

Tol_Pairs_M8.plot<-ggplot(data=Tol_contrasts[which(Tol_contrasts$TimeP=="M8"),], aes(x=Response, y=Contrast, fill=Estimate))+
  geom_tile()+
  geom_text(aes(label=Sig), size=sig.sz)+
  scale_fill_gradient2(low="#BA1E02FF", mid="white", high="#3BA0FDFF")+
  theme_bw()+
  ggtitle("Pairwise Contrasts Month 8")+
  theme(axis.text.x=element_text(size=axis.title.sz, colour="black", angle=45, hjust=1),
        axis.text.y=element_text(size=axis.txt.sz-1, colour="black"),
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="", fill="Estimate");Tol_Pairs_M8.plot

Month 12

Tol_Pairs_M12.plot<-ggplot(data=Tol_contrasts[which(Tol_contrasts$TimeP=="M12"),], aes(x=Response, y=Contrast, fill=Estimate))+
  geom_tile()+
  geom_text(aes(label=Sig), size=sig.sz)+
  scale_fill_gradient2(low="#BA1E02FF", mid="white", high="#3BA0FDFF")+
  theme_bw()+
  ggtitle("Pairwise Contrasts Month 12")+
  theme(axis.text.x=element_text(size=axis.title.sz, colour="black", angle=45, hjust=1),
        axis.text.y=element_text(size=axis.txt.sz-1, colour="black"),
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="", fill="Estimate");Tol_Pairs_M12.plot

Significant Differences

Subsetting Contrasts to only include contrasts with significant differences in at least one of the thermal tolerance metrics (Chlorophyll or Symbiont or Color retention)

##Add Set column with Contrast and Timepoint
Tol_contrasts$Set<-paste(Tol_contrasts$TimeP, Tol_contrasts$Contrast, sep=" ")

##Remove rows with non-significant p-values
Tol_contrasts_sig<-subset(Tol_contrasts, p < 0.05)

##Keep Sets where at least one metric shows a significant difference
Tol_Sets_sig<-data.frame(Set=c(unique(Tol_contrasts_sig$Set)))
Tol_contrasts_sig_Set<-merge(Tol_Sets_sig, Tol_contrasts, all.x=TRUE, all.y=FALSE)

##Re-order by Timepoint
Tol_contrasts_sig_Set$TimeP<-factor(Tol_contrasts_sig_Set$TimeP, levels=c("W1", "W2", "M1", "M4", "M8", "M12"), ordered=TRUE)
Tol_contrasts_sig_Set<- Tol_contrasts_sig_Set %>% arrange(desc(as.numeric(TimeP)))

Tol_contrasts_sig_Set$Set<-factor(Tol_contrasts_sig_Set$Set, levels=c(unique(Tol_contrasts_sig_Set$Set)), ordered=TRUE)
Tol_Pairs_sig.plot<-ggplot(data=Tol_contrasts_sig_Set, aes(x=Response, y=Set, fill=Estimate))+
  geom_tile()+
  geom_text(aes(label=Sig), size=sig.sz)+
  scale_fill_gradient2(low="#BA1E02FF", mid="white", high="#3BA0FDFF")+
  theme_bw()+
  ggtitle("Significant Pairwise Contrasts")+
  theme(axis.text.x=element_text(size=axis.title.sz, colour="black", angle=45, hjust=1),
        axis.text.y=element_text(size=axis.txt.sz-1, colour="black"),
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="", fill="Estimate");Tol_Pairs_sig.plot

Figures

Week 1

##Create Panel
Tolerance_W1_fig<-plot_grid(Tol_Pairs_W1.plot, Tol_Chl_W1_SG.plot,
                        Tol_ScoreF_W1_SG.plot, Tol_ScoreTP_W1_SG.plot, 
                        nrow=1, ncol=4, 
                        rel_widths=c(1, 0.75, 0.75, 0.75), rel_heights=1, 
                        labels=c("A", "B", "C", "D"), 
                        label_size=panel.lab.sz, 
                        label_fontface = "bold")
Warning: Removed 17 rows containing missing values (`geom_text()`).
##Save Figure
ggsave(filename="Figures/Tolerance_W1.png", plot=Tolerance_W1_fig, dpi=300, width=20, height=6, units="in")

Week 2

##Create Panel
Tolerance_W2_fig<-plot_grid(Tol_Pairs_W2.plot, 
                        Tol_Chl_W2_SG.plot, Tol_ScoreF_W2_SG.plot,
                        Tol_ScoreTP_W2_SG.plot, Tol_Sym_W2_SG.plot, 
                        nrow=1, ncol=5, 
                        rel_widths=c(1, 0.75, 0.75, 0.75, 0.75),
                        rel_heights=1, 
                        labels=c("A", "B", "C", "D", "E"), 
                        label_size=panel.lab.sz, 
                        label_fontface = "bold")
Warning: Removed 21 rows containing missing values (`geom_text()`).
##Save Figure
ggsave(filename="Figures/Tolerance_W2.png", plot=Tolerance_W2_fig, dpi=300, width=25, height=6, units="in")

Month 1

##Create Panel
Tolerance_M1_fig<-plot_grid(Tol_Pairs_M1.plot, 
                        Tol_Chl_M1_SG.plot, Tol_ScoreF_M1_SG.plot,
                        Tol_ScoreTP_M1_SG.plot, Tol_Sym_M1_SG.plot, 
                        nrow=1, ncol=5, 
                        rel_widths=c(1, 0.75, 0.75, 0.75, 0.75),
                        rel_heights=1, 
                        labels=c("A", "B", "C", "D", "E"), 
                        label_size=panel.lab.sz, 
                        label_fontface = "bold")
Warning: Removed 17 rows containing missing values (`geom_text()`).
##Save Figure
ggsave(filename="Figures/Tolerance_M1.png", plot=Tolerance_M1_fig, dpi=300, width=25, height=6, units="in")

Month 4

##Create Panel
Tolerance_M4_fig<-plot_grid(Tol_Pairs_M4.plot, 
                        Tol_Chl_M4_SG.plot, Tol_ScoreF_M4_SG.plot,
                        Tol_ScoreTP_M4_SG.plot, Tol_Sym_M4_SG.plot, 
                        nrow=1, ncol=5, 
                        rel_widths=c(1, 0.75, 0.75, 0.75, 0.75),
                        rel_heights=1, 
                        labels=c("A", "B", "C", "D", "E"), 
                        label_size=panel.lab.sz, 
                        label_fontface = "bold")
Warning: Removed 16 rows containing missing values (`geom_text()`).
##Save Figure
ggsave(filename="Figures/Tolerance_M4.png", plot=Tolerance_M4_fig, dpi=300, width=25, height=6, units="in")

Month 8

##Create Panel
Tolerance_M8_fig<-plot_grid(Tol_Pairs_M8.plot, 
                        Tol_Chl_M8_SG.plot, Tol_ScoreF_M8_SG.plot,
                        Tol_ScoreTP_M8_SG.plot, Tol_Sym_M8_SG.plot, 
                        nrow=1, ncol=5, 
                        rel_widths=c(1, 0.75, 0.75, 0.75, 0.75),
                        rel_heights=1, 
                        labels=c("A", "B", "C", "D", "E"), 
                        label_size=panel.lab.sz, 
                        label_fontface = "bold")
Warning: Removed 29 rows containing missing values (`geom_text()`).
##Save Figure
ggsave(filename="Figures/Tolerance_M8.png", plot=Tolerance_M8_fig, dpi=300, width=25, height=6, units="in")

Month 12

##Create Panel
Tolerance_M12_fig<-plot_grid(Tol_Pairs_M12.plot, 
                        Tol_Chl_M12_SG.plot, Tol_ScoreF_M12_SG.plot,
                        Tol_ScoreTP_M12_SG.plot, Tol_Sym_M12_SG.plot, 
                        nrow=1, ncol=5, 
                        rel_widths=c(1, 0.75, 0.75, 0.75, 0.75),
                        rel_heights=1, 
                        labels=c("A", "B", "C", "D", "E"), 
                        label_size=panel.lab.sz, 
                        label_fontface = "bold")
Warning: Removed 36 rows containing missing values (`geom_text()`).
##Save Figure
ggsave(filename="Figures/Tolerance_M12.png", plot=Tolerance_M12_fig, dpi=300, width=25, height=6, units="in")

Significant Contrasts Heatmap

##Save Figure
ggsave(filename="Figures/Tolerance_Heatmap.png", plot=Tol_Pairs_sig.plot, dpi=300, width=8, height=12, units="in")

Tables

Linear Model Results

##Combine Results Tables
TableS2_Tol.lm.res<-rbind(Tol_Chl_W1_lm.res, Tol_Chl_W2_lm.res, 
                          Tol_Chl_M1_lm.res, Tol_Chl_M4_lm.res,
                          Tol_Chl_M8_lm.res, Tol_Chl_M12_lm.res,
                          Tol_Sym_W2_lm.res, 
                          Tol_Sym_M1_lm.res, Tol_Sym_M4_lm.res,
                          Tol_Sym_M8_lm.res, Tol_Sym_M12_lm.res,
                          Tol_ScoreF_W1_lm.res, Tol_ScoreF_W2_lm.res, 
                          Tol_ScoreF_M1_lm.res, Tol_ScoreF_M4_lm.res,
                          Tol_ScoreF_M8_lm.res, Tol_ScoreF_M12_lm.res,
                          Tol_ScoreTP_W1_lm.res, Tol_ScoreTP_W2_lm.res, 
                          Tol_ScoreTP_M1_lm.res, Tol_ScoreTP_M4_lm.res,
                          Tol_ScoreTP_M8_lm.res, Tol_ScoreTP_M12_lm.res)

##Organize
names(TableS2_Tol.lm.res)
[1] "DF"        "Sum.Sq"    "Mean.Sq"   "F.value"   "p.value"   "Predictor"
[7] "EtaSq"     "Response"  "TimeP"    
TableS2_Tol.lm.res<-TableS2_Tol.lm.res[,c("TimeP", "Response", "Predictor", "DF", "Sum.Sq", "Mean.Sq", "F.value", "p.value", "EtaSq")]

#Round to 4 digits
TableS2_Tol.lm.res$Sum.Sq<-round(TableS2_Tol.lm.res$Sum.Sq, 4)
TableS2_Tol.lm.res$Mean.Sq<-round(TableS2_Tol.lm.res$Mean.Sq, 4)
TableS2_Tol.lm.res$F.value<-round(TableS2_Tol.lm.res$F.value, 4)
TableS2_Tol.lm.res$p.value<-round(TableS2_Tol.lm.res$p.value, 4)
TableS2_Tol.lm.res$EtaSq<-round(TableS2_Tol.lm.res$EtaSq, 4)

##Write Out Table
write.csv(TableS2_Tol.lm.res, "Tables/TableS2_Tolerance_LM_Results.csv", row.names=FALSE)

Pairwise Comparison Results

##Pairwise Results Table
TableS3_Tol.pairwise<-Tol_contrasts

##Organize
names(TableS3_Tol.pairwise)
 [1] "group1"   "group2"   "estimate" "SE"       "df"       "t.ratio"  "p"       
 [8] "Sig"      "Response" "TimeP"    "Contrast" "Estimate" "Set"     
TableS3_Tol.pairwise<-TableS3_Tol.pairwise[,c("TimeP", "Response", "Contrast", "Estimate", "SE", "df", "t.ratio", "p")]
TableS3_Tol.pairwise<-TableS3_Tol.pairwise %>% dplyr::rename( DF = df) %>% dplyr::rename( p.value = p)

#Round to 4 digits
TableS3_Tol.pairwise$Estimate<-round(TableS3_Tol.pairwise$Estimate, 4)
TableS3_Tol.pairwise$SE<-round(TableS3_Tol.pairwise$SE, 4)
TableS3_Tol.pairwise$t.ratio<-round(TableS3_Tol.pairwise$t.ratio, 4)
TableS3_Tol.pairwise$p.value<-round(TableS3_Tol.pairwise$p.value, 4)

##Write Out Table
write.csv(TableS3_Tol.pairwise, "Tables/TableS3_Tolerance_Pairwise_Results.csv", row.names=FALSE)
---
title: "Thermal Tolerance with Color Score and Bleaching Metrics"
author: "Serena Hackerott and Lauren Gregory"
date: "7/19/2024"
output:
  html_document:
    toc: yes
    df_print: paged
  html_notebook:
    toc: yes
    toc_float: yes
---

# Setup
```{r Setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
```


### Load Packages
```{r}
##Install Packages if Needed
if (!require("ggplot2")) install.packages("ggplot2")
if (!require("effectsize")) install.packages("effectsize")
if (!require("emmeans")) install.packages("emmeans")
if (!require("dplyr")) install.packages("dplyr")
if (!require("tidyr")) install.packages("tidyr")
if (!require("Rmisc")) install.packages("Rmisc")
if (!require("ggpubr")) install.packages("ggpubr")
if (!require("cowplot")) install.packages("cowplot")
if (!require("gridGraphics")) install.packages("gridGraphics")
if (!require("tidyr")) install.packages("tidyr")

##Load Packages
library(ggplot2) #Required for plotting
library(effectsize) #Required for eta_squared effect sizes
library(emmeans) #Required for pairwise comparisons
library(dplyr) #Required to seperate columns in dataframe
library(tidyr) #Required for data organization
library(Rmisc) #Required for summarySE for summary statistics
library(ggpubr) #Required for adding pairwise p-values to plots with stat_pvalue_manual 
library(cowplot) #Required for arranging ggplots 
library(gridGraphics) #Required for adding labels to arranged plots
library(tidyr) #Required for reshaping datafrom from a wide to long format.
```
Note: Run "Graphing Parameters" section from 01_ExperimentalSetup.R file


### Load and Organize Data
Note: Full Data with Bleaching Metrics and Color Scores created in 04_Models.R file
```{r}
#Load Data
FullData<-read.csv("Outputs/FullData.csv", header=TRUE)


#Set factor variables 
FullData$TimeP<-factor(FullData$TimeP, levels=c("W1", "W2", "M1", "M4", "M8", "M12"), ordered=TRUE)
FullData$Site<-factor(FullData$Site, levels=c("KL", "SS"), ordered=TRUE)
FullData$Genotype<-factor(FullData$Genotype, levels=c("AC10", "AC12", "AC8"), ordered=TRUE)
FullData$Treatment<-factor(FullData$Treatment, levels=c("Control", "Heat"), ordered=TRUE)
FullData$Treat<-factor(FullData$Treat, levels=c("C", "H"), ordered=TRUE)

```


# Percent Retention

### Subset Thermal Assay Data by Treatment
```{r}
##Control
FullData_C<-subset(FullData, Treat=="C")

##Heated
FullData_H<-subset(FullData, Treat=="H")
```


### Calculate Percent Retention
Calculating retention as proportion remaining relative to corresponding control levels (0-1) for each site and genotype at each timepoint.
```{r}
##Calculate averages for Control Treatment for each Site, Genotype, and Timepoint 
names(FullData_C)
FullData_C.a<-aggregate(FullData_C[,c(9:10, 16:17)], list(FullData_C$Site, FullData_C$Genotype, FullData_C$TimeP), mean, na.action = na.omit)
names(FullData_C.a)[1:3]<-c("Site", "Genotype", "TimeP")
names(FullData_C.a)[4:7]<-paste(names(FullData_C)[c(9:10, 16:17)], "C", sep="_")

##Merge Control Averages with Heated Samples
#Merges by Site, Genotype, and Timepoint
TolData<-merge(FullData_H, FullData_C.a, all.x=TRUE )

##Calculate Proportion Retained for each Bleaching Metric relative to Control
TolData$Score_Full.prop<-round((TolData$Score_Full/TolData$Score_Full_C), 4)
TolData$Score_TP.prop<-round((TolData$Score_TP/TolData$Score_TP_C), 4)
TolData$Chl.prop<-round((TolData$Chl_ug.cm2/TolData$Chl_ug.cm2_C), 4)
TolData$Sym.prop<-round((TolData$Sym10.6_cm2/TolData$Sym10.6_cm2_C), 4)

##Set values >1 to 1
TolData$Score_Full.prop[which(TolData$Score_Full.prop>1)]<-1.0000
TolData$Score_TP.prop[which(TolData$Score_TP.prop>1)]<-1.0000
TolData$Chl.prop[which(TolData$Chl.prop>1)]<-1.0000
TolData$Sym.prop[which(TolData$Sym.prop>1)]<-1.0000

##Create Site and Genotype Variable
TolData$Site.Geno<-paste(TolData$Site, TolData$Genotype, sep="_")
```


### Subset by Timepoint
```{r}
TolData_W1<-subset(TolData, TimeP=="W1")
TolData_W2<-subset(TolData, TimeP=="W2")
TolData_M1<-subset(TolData, TimeP=="M1")
TolData_M4<-subset(TolData, TimeP=="M4")
TolData_M8<-subset(TolData, TimeP=="M8")
TolData_M12<-subset(TolData, TimeP=="M12")

```



# Thermal Tolerance Chlorophyll

## Full Set

### Run Model
```{r}
##Check normality
hist(TolData$Chl.prop)
shapiro.test(TolData$Chl.prop)
#Not Normal

##Try log transformation
hist(log(TolData$Chl.prop))
shapiro.test(log(TolData$Chl.prop))
#Not normal but improved

##Try square root transformation
hist(sqrt(TolData$Chl.prop))
shapiro.test(log(TolData$Chl.prop))
#Not normal but much improved


##Model as a function of Site and Genotype and Timepoint
##Model with sqrt transformation
Tol_Chl_lm<-lm(sqrt(Chl.prop)~Site+Genotype+TimeP+ Site:Genotype + Site:TimeP + Genotype:TimeP, data=TolData)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Chl_lm)))

#Q-Q plot
qqnorm(resid(Tol_Chl_lm)); qqline(resid(Tol_Chl_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Chl_lm), resid(Tol_Chl_lm))

```


### Model Results

```{r}
#Model Results
summary(Tol_Chl_lm)
anova(Tol_Chl_lm)

#Effect Size of Predictors
eta_squared(Tol_Chl_lm, partial=FALSE)

##Save model results
Tol_Chl_lm.res<-data.frame(anova(Tol_Chl_lm))
Tol_Chl_lm.res$Predictor<-rownames(Tol_Chl_lm.res)
Tol_Chl_lm.res$EtaSq<-c(eta_squared(Tol_Chl_lm, partial=FALSE)$Eta2, NA)
Tol_Chl_lm.res$Response<-rep("Chlorophyll", nrow(Tol_Chl_lm.res))
Tol_Chl_lm.res<-Tol_Chl_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```
Strong influence of Timepoint, including interactions with main variables of interest, Site and Genotype. Will analyze each Timepoint individually. 


## Chl Timepoint W1

### Run Model
```{r}
##Check normality
hist(TolData_W1$Chl.prop)
shapiro.test(TolData_W1$Chl.prop)
#Not Normal

##Try log+1 transformation
hist(log(TolData_W1$Chl.prop+1))
shapiro.test(log(TolData_W1$Chl.prop+1))
#Normal

##Model as a function of Site and Genotype
##Model with log+1 transformation
Tol_Chl_W1_lm<-lm(log(Chl.prop+1)~Site+Genotype+Site:Genotype, data=TolData_W1)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Chl_W1_lm)))

#Q-Q plot
qqnorm(resid(Tol_Chl_W1_lm)); qqline(resid(Tol_Chl_W1_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Chl_W1_lm), resid(Tol_Chl_W1_lm))

```


### Model Results

#### Overall
```{r}
#Model Results
summary(Tol_Chl_W1_lm)
anova(Tol_Chl_W1_lm)

#Effect Size of Predictors
eta_squared(Tol_Chl_W1_lm, partial=FALSE)

##Save model results
Tol_Chl_W1_lm.res<-data.frame(anova(Tol_Chl_W1_lm))
Tol_Chl_W1_lm.res$Predictor<-rownames(Tol_Chl_W1_lm.res)
Tol_Chl_W1_lm.res$EtaSq<-c(eta_squared(Tol_Chl_W1_lm, partial=FALSE)$Eta2, NA)
Tol_Chl_W1_lm.res$Response<-rep("Chlorophyll", nrow(Tol_Chl_W1_lm.res))
Tol_Chl_W1_lm.res$TimeP<-rep("W1", nrow(Tol_Chl_W1_lm.res))
Tol_Chl_W1_lm.res<-Tol_Chl_W1_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```

Significant effect of Site and Genotype and Site*Genotype.


#### Pairwise
```{r}
#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_Chl_W1_lm, pairwise~Genotype | Site)

#Sites within Genotypes
emmeans(Tol_Chl_W1_lm, pairwise~Site | Genotype)

##Save p-values

#Genotypes within Sites
Tol_Chl_W1_lm.geno<-data.frame(emmeans(Tol_Chl_W1_lm, pairwise~Genotype | Site)$contrasts)
Tol_Chl_W1_lm.geno<-Tol_Chl_W1_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_W1_lm.geno$group1<-paste(Tol_Chl_W1_lm.geno$Site, Tol_Chl_W1_lm.geno$group1, sep="_")
Tol_Chl_W1_lm.geno$group2<-paste(Tol_Chl_W1_lm.geno$Site, Tol_Chl_W1_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_Chl_W1_lm.site<-data.frame(emmeans(Tol_Chl_W1_lm, pairwise~Site | Genotype)$contrasts)
Tol_Chl_W1_lm.site<-Tol_Chl_W1_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_W1_lm.site$group1<-paste(Tol_Chl_W1_lm.site$group1, Tol_Chl_W1_lm.site$Genotype, sep="_")
Tol_Chl_W1_lm.site$group2<-paste(Tol_Chl_W1_lm.site$group2, Tol_Chl_W1_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_Chl_W1_lm.p<-rbind(Tol_Chl_W1_lm.geno[,c(1:2,4:8)], Tol_Chl_W1_lm.site[,c(1:2,4:8)])
Tol_Chl_W1_lm.p<-Tol_Chl_W1_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_Chl_W1_lm.p$Sig<-ifelse(Tol_Chl_W1_lm.p$p<0.001, "***", ifelse(Tol_Chl_W1_lm.p$p<0.01, "**", ifelse(Tol_Chl_W1_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_Chl_W1_lm.p$Response<-rep("Chlorophyll", nrow(Tol_Chl_W1_lm.p))
Tol_Chl_W1_lm.p$TimeP<-rep("W1", nrow(Tol_Chl_W1_lm.p))

```


### Plot Retention by Site and Genotype
```{r}
##Summary statistics by Site and Genotype
Tol_Chl_W1_SG<-summarySE(TolData_W1, measurevar="Chl.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_Chl_W1_SG.plot<-ggplot(Tol_Chl_W1_SG, aes(x=Site.Geno, y=Chl.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Chlorophyll Retention")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_Chl_W1_lm.p,  y.position=0.4, step.increase=0.25, label="Sig", hide.ns=TRUE); Tol_Chl_W1_SG.plot

```


## Chl Timepoint W2

### Run Model
```{r}
##Check normality
hist(TolData_W2$Chl.prop)
shapiro.test(TolData_W2$Chl.prop)
#Normal

##Model as a function of Site and Genotype
Tol_Chl_W2_lm<-lm(Chl.prop~Site+Genotype+Site:Genotype, data=TolData_W2)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Chl_W2_lm)))

#Q-Q plot
qqnorm(resid(Tol_Chl_W2_lm)); qqline(resid(Tol_Chl_W2_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Chl_W2_lm), resid(Tol_Chl_W2_lm))

```


### Model Results

#### Overall
```{r}
#Model Results
summary(Tol_Chl_W2_lm)
anova(Tol_Chl_W2_lm)

#Effect Size of Predictors
eta_squared(Tol_Chl_W2_lm, partial=FALSE)

##Save model results
Tol_Chl_W2_lm.res<-data.frame(anova(Tol_Chl_W2_lm))
Tol_Chl_W2_lm.res$Predictor<-rownames(Tol_Chl_W2_lm.res)
Tol_Chl_W2_lm.res$EtaSq<-c(eta_squared(Tol_Chl_W2_lm, partial=FALSE)$Eta2, NA)
Tol_Chl_W2_lm.res$Response<-rep("Chlorophyll", nrow(Tol_Chl_W2_lm.res))
Tol_Chl_W2_lm.res$TimeP<-rep("W2", nrow(Tol_Chl_W2_lm.res))
Tol_Chl_W2_lm.res<-Tol_Chl_W2_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```

Significant effect of Genotype. Still checking Site*Genotype for comparability across Timepoints.


#### Pairwise
```{r}
#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_Chl_W2_lm, pairwise~Genotype | Site)

#Sites within Genotypes
emmeans(Tol_Chl_W2_lm, pairwise~Site | Genotype)

##Save p-values

#Genotypes within Sites
Tol_Chl_W2_lm.geno<-data.frame(emmeans(Tol_Chl_W2_lm, pairwise~Genotype | Site)$contrasts)
Tol_Chl_W2_lm.geno<-Tol_Chl_W2_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_W2_lm.geno$group1<-paste(Tol_Chl_W2_lm.geno$Site, Tol_Chl_W2_lm.geno$group1, sep="_")
Tol_Chl_W2_lm.geno$group2<-paste(Tol_Chl_W2_lm.geno$Site, Tol_Chl_W2_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_Chl_W2_lm.site<-data.frame(emmeans(Tol_Chl_W2_lm, pairwise~Site | Genotype)$contrasts)
Tol_Chl_W2_lm.site<-Tol_Chl_W2_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_W2_lm.site$group1<-paste(Tol_Chl_W2_lm.site$group1, Tol_Chl_W2_lm.site$Genotype, sep="_")
Tol_Chl_W2_lm.site$group2<-paste(Tol_Chl_W2_lm.site$group2, Tol_Chl_W2_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_Chl_W2_lm.p<-rbind(Tol_Chl_W2_lm.geno[,c(1:2,4:8)], Tol_Chl_W2_lm.site[,c(1:2,4:8)])
Tol_Chl_W2_lm.p<-Tol_Chl_W2_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_Chl_W2_lm.p$Sig<-ifelse(Tol_Chl_W2_lm.p$p<0.001, "***", ifelse(Tol_Chl_W2_lm.p$p<0.01, "**", ifelse(Tol_Chl_W2_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_Chl_W2_lm.p$Response<-rep("Chlorophyll", nrow(Tol_Chl_W2_lm.p))
Tol_Chl_W2_lm.p$TimeP<-rep("W2", nrow(Tol_Chl_W2_lm.p))

```


### Plot Retention by Site and Genotype
```{r}
##Summary statistics by Site and Genotype
Tol_Chl_W2_SG<-summarySE(TolData_W2, measurevar="Chl.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_Chl_W2_SG.plot<-ggplot(Tol_Chl_W2_SG, aes(x=Site.Geno, y=Chl.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Chlorophyll Retention")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_Chl_W2_lm.p,  y.position=0.4, step.increase=0.25, label="Sig", hide.ns=TRUE); Tol_Chl_W2_SG.plot

```


## Chl Timepoint M1

### Run Model
```{r}
##Check normality
hist(TolData_M1$Chl.prop)
shapiro.test(TolData_M1$Chl.prop)
#Normal

##Model as a function of Site and Genotype
Tol_Chl_M1_lm<-lm(Chl.prop~Site+Genotype+Site:Genotype, data=TolData_M1)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Chl_M1_lm)))

#Q-Q plot
qqnorm(resid(Tol_Chl_M1_lm)); qqline(resid(Tol_Chl_M1_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Chl_M1_lm), resid(Tol_Chl_M1_lm))

```


### Model Results

#### Overall
```{r}
#Model Results
summary(Tol_Chl_M1_lm)
anova(Tol_Chl_M1_lm)

#Effect Size of Predictors
eta_squared(Tol_Chl_M1_lm, partial=FALSE)

##Save model results
Tol_Chl_M1_lm.res<-data.frame(anova(Tol_Chl_M1_lm))
Tol_Chl_M1_lm.res$Predictor<-rownames(Tol_Chl_M1_lm.res)
Tol_Chl_M1_lm.res$EtaSq<-c(eta_squared(Tol_Chl_M1_lm, partial=FALSE)$Eta2, NA)
Tol_Chl_M1_lm.res$Response<-rep("Chlorophyll", nrow(Tol_Chl_M1_lm.res))
Tol_Chl_M1_lm.res$TimeP<-rep("M1", nrow(Tol_Chl_M1_lm.res))
Tol_Chl_M1_lm.res<-Tol_Chl_M1_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```

Significant effect of Genotype. Still checking Site*Genotype for comparability across Timepoints.


#### Pairwise
```{r}
#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_Chl_M1_lm, pairwise~Genotype | Site)

#Sites within Genotypes
emmeans(Tol_Chl_M1_lm, pairwise~Site | Genotype)

##Save p-values

#Genotypes within Sites
Tol_Chl_M1_lm.geno<-data.frame(emmeans(Tol_Chl_M1_lm, pairwise~Genotype | Site)$contrasts)
Tol_Chl_M1_lm.geno<-Tol_Chl_M1_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_M1_lm.geno$group1<-paste(Tol_Chl_M1_lm.geno$Site, Tol_Chl_M1_lm.geno$group1, sep="_")
Tol_Chl_M1_lm.geno$group2<-paste(Tol_Chl_M1_lm.geno$Site, Tol_Chl_M1_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_Chl_M1_lm.site<-data.frame(emmeans(Tol_Chl_M1_lm, pairwise~Site | Genotype)$contrasts)
Tol_Chl_M1_lm.site<-Tol_Chl_M1_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_M1_lm.site$group1<-paste(Tol_Chl_M1_lm.site$group1, Tol_Chl_M1_lm.site$Genotype, sep="_")
Tol_Chl_M1_lm.site$group2<-paste(Tol_Chl_M1_lm.site$group2, Tol_Chl_M1_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_Chl_M1_lm.p<-rbind(Tol_Chl_M1_lm.geno[,c(1:2,4:8)], Tol_Chl_M1_lm.site[,c(1:2,4:8)])
Tol_Chl_M1_lm.p<-Tol_Chl_M1_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_Chl_M1_lm.p$Sig<-ifelse(Tol_Chl_M1_lm.p$p<0.001, "***", ifelse(Tol_Chl_M1_lm.p$p<0.01, "**", ifelse(Tol_Chl_M1_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_Chl_M1_lm.p$Response<-rep("Chlorophyll", nrow(Tol_Chl_M1_lm.p))
Tol_Chl_M1_lm.p$TimeP<-rep("M1", nrow(Tol_Chl_M1_lm.p))

```


### Plot Retention by Site and Genotype
```{r}
##Summary statistics by Site and Genotype
Tol_Chl_M1_SG<-summarySE(TolData_M1, measurevar="Chl.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_Chl_M1_SG.plot<-ggplot(Tol_Chl_M1_SG, aes(x=Site.Geno, y=Chl.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Chlorophyll Retention")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_Chl_M1_lm.p,  y.position=0.4, step.increase=0.25, label="Sig", hide.ns=TRUE); Tol_Chl_M1_SG.plot

```


## Chl Timepoint M4

### Run Model
```{r}
##Check normality
hist(TolData_M4$Chl.prop)
shapiro.test(TolData_M4$Chl.prop)
#Not Normal

##Try log+1 transformation
hist(log(TolData_M4$Chl.prop+1))
shapiro.test(log(TolData_M4$Chl.prop+1))
#Normal

##Model as a function of Site and Genotype
##Model with log transformation
Tol_Chl_M4_lm<-lm(log(Chl.prop+1)~Site+Genotype+Site:Genotype, data=TolData_M4)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Chl_M4_lm)))

#Q-Q plot
qqnorm(resid(Tol_Chl_M4_lm)); qqline(resid(Tol_Chl_M4_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Chl_M4_lm), resid(Tol_Chl_M4_lm))

```


### Model Results

#### Overall
```{r}
#Model Results
summary(Tol_Chl_M4_lm)
anova(Tol_Chl_M4_lm)

#Effect Size of Predictors
eta_squared(Tol_Chl_M4_lm, partial=FALSE)

##Save model results
Tol_Chl_M4_lm.res<-data.frame(anova(Tol_Chl_M4_lm))
Tol_Chl_M4_lm.res$Predictor<-rownames(Tol_Chl_M4_lm.res)
Tol_Chl_M4_lm.res$EtaSq<-c(eta_squared(Tol_Chl_M4_lm, partial=FALSE)$Eta2, NA)
Tol_Chl_M4_lm.res$Response<-rep("Chlorophyll", nrow(Tol_Chl_M4_lm.res))
Tol_Chl_M4_lm.res$TimeP<-rep("M4", nrow(Tol_Chl_M4_lm.res))
Tol_Chl_M4_lm.res<-Tol_Chl_M4_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```

Significant effect of Site and Genotype and Site*Genotype.


#### Pairwise
```{r}
#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_Chl_M4_lm, pairwise~Genotype | Site)

#Sites within Genotypes
emmeans(Tol_Chl_M4_lm, pairwise~Site | Genotype)

##Save p-values

#Genotypes within Sites
Tol_Chl_M4_lm.geno<-data.frame(emmeans(Tol_Chl_M4_lm, pairwise~Genotype | Site)$contrasts)
Tol_Chl_M4_lm.geno<-Tol_Chl_M4_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_M4_lm.geno$group1<-paste(Tol_Chl_M4_lm.geno$Site, Tol_Chl_M4_lm.geno$group1, sep="_")
Tol_Chl_M4_lm.geno$group2<-paste(Tol_Chl_M4_lm.geno$Site, Tol_Chl_M4_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_Chl_M4_lm.site<-data.frame(emmeans(Tol_Chl_M4_lm, pairwise~Site | Genotype)$contrasts)
Tol_Chl_M4_lm.site<-Tol_Chl_M4_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_M4_lm.site$group1<-paste(Tol_Chl_M4_lm.site$group1, Tol_Chl_M4_lm.site$Genotype, sep="_")
Tol_Chl_M4_lm.site$group2<-paste(Tol_Chl_M4_lm.site$group2, Tol_Chl_M4_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_Chl_M4_lm.p<-rbind(Tol_Chl_M4_lm.geno[,c(1:2,4:8)], Tol_Chl_M4_lm.site[,c(1:2,4:8)])
Tol_Chl_M4_lm.p<-Tol_Chl_M4_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_Chl_M4_lm.p$Sig<-ifelse(Tol_Chl_M4_lm.p$p<0.001, "***", ifelse(Tol_Chl_M4_lm.p$p<0.01, "**", ifelse(Tol_Chl_M4_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_Chl_M4_lm.p$Response<-rep("Chlorophyll", nrow(Tol_Chl_M4_lm.p))
Tol_Chl_M4_lm.p$TimeP<-rep("M4", nrow(Tol_Chl_M4_lm.p))

```


### Plot Retention by Site and Genotype
```{r}
##Summary statistics by Site and Genotype
Tol_Chl_M4_SG<-summarySE(TolData_M4, measurevar="Chl.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_Chl_M4_SG.plot<-ggplot(Tol_Chl_M4_SG, aes(x=Site.Geno, y=Chl.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Chlorophyll Retention")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_Chl_M4_lm.p,  y.position=0.55, step.increase=0.20, label="Sig", hide.ns=TRUE); Tol_Chl_M4_SG.plot

```


## Chl Timepoint M8

### Run Model
```{r}
##Check normality
hist(TolData_M8$Chl.prop)
shapiro.test(TolData_M8$Chl.prop)
#Normal

##Model as a function of Site and Genotype
Tol_Chl_M8_lm<-lm(Chl.prop~Site+Genotype+Site:Genotype, data=TolData_M8)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Chl_M8_lm)))

#Q-Q plot
qqnorm(resid(Tol_Chl_M8_lm)); qqline(resid(Tol_Chl_M8_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Chl_M8_lm), resid(Tol_Chl_M8_lm))

```


### Model Results

#### Overall
```{r}
#Model Results
summary(Tol_Chl_M8_lm)
anova(Tol_Chl_M8_lm)

#Effect Size of Predictors
eta_squared(Tol_Chl_M8_lm, partial=FALSE)

##Save model results
Tol_Chl_M8_lm.res<-data.frame(anova(Tol_Chl_M8_lm))
Tol_Chl_M8_lm.res$Predictor<-rownames(Tol_Chl_M8_lm.res)
Tol_Chl_M8_lm.res$EtaSq<-c(eta_squared(Tol_Chl_M8_lm, partial=FALSE)$Eta2, NA)
Tol_Chl_M8_lm.res$Response<-rep("Chlorophyll", nrow(Tol_Chl_M8_lm.res))
Tol_Chl_M8_lm.res$TimeP<-rep("M8", nrow(Tol_Chl_M8_lm.res))
Tol_Chl_M8_lm.res<-Tol_Chl_M8_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```

Significant effect of Genotype. Marginal effect (p<0.1) of Site * Genotype. Still checking Site*Genotype for comparability across Timepoints.


#### Pairwise
```{r}
#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_Chl_M8_lm, pairwise~Genotype | Site)

#Sites within Genotypes
emmeans(Tol_Chl_M8_lm, pairwise~Site | Genotype)

##Save p-values

#Genotypes within Sites
Tol_Chl_M8_lm.geno<-data.frame(emmeans(Tol_Chl_M8_lm, pairwise~Genotype | Site)$contrasts)
Tol_Chl_M8_lm.geno<-Tol_Chl_M8_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_M8_lm.geno$group1<-paste(Tol_Chl_M8_lm.geno$Site, Tol_Chl_M8_lm.geno$group1, sep="_")
Tol_Chl_M8_lm.geno$group2<-paste(Tol_Chl_M8_lm.geno$Site, Tol_Chl_M8_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_Chl_M8_lm.site<-data.frame(emmeans(Tol_Chl_M8_lm, pairwise~Site | Genotype)$contrasts)
Tol_Chl_M8_lm.site<-Tol_Chl_M8_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_M8_lm.site$group1<-paste(Tol_Chl_M8_lm.site$group1, Tol_Chl_M8_lm.site$Genotype, sep="_")
Tol_Chl_M8_lm.site$group2<-paste(Tol_Chl_M8_lm.site$group2, Tol_Chl_M8_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_Chl_M8_lm.p<-rbind(Tol_Chl_M8_lm.geno[,c(1:2,4:8)], Tol_Chl_M8_lm.site[,c(1:2,4:8)])
Tol_Chl_M8_lm.p<-Tol_Chl_M8_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_Chl_M8_lm.p$Sig<-ifelse(Tol_Chl_M8_lm.p$p<0.001, "***", ifelse(Tol_Chl_M8_lm.p$p<0.01, "**", ifelse(Tol_Chl_M8_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_Chl_M8_lm.p$Response<-rep("Chlorophyll", nrow(Tol_Chl_M8_lm.p))
Tol_Chl_M8_lm.p$TimeP<-rep("M8", nrow(Tol_Chl_M8_lm.p))

```


### Plot Retention by Site and Genotype
```{r}
##Summary statistics by Site and Genotype
Tol_Chl_M8_SG<-summarySE(TolData_M8, measurevar="Chl.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_Chl_M8_SG.plot<-ggplot(Tol_Chl_M8_SG, aes(x=Site.Geno, y=Chl.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Chlorophyll Retention")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_Chl_M8_lm.p,  y.position=0.65, step.increase=0.6, label="Sig", hide.ns=TRUE); Tol_Chl_M8_SG.plot

```


## Chl Timepoint M12

### Run Model
```{r}
##Check normality
hist(TolData_M12$Chl.prop)
shapiro.test(TolData_M12$Chl.prop)
#Not Normal

##Try log+1 transformation
hist(log(TolData_M12$Chl.prop+1))
shapiro.test(log(TolData_M12$Chl.prop+1))
#Normal

##Model as a function of Site and Genotype
##Model with log transformation
Tol_Chl_M12_lm<-lm(log(Chl.prop+1)~Site+Genotype+Site:Genotype, data=TolData_M12)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Chl_M12_lm)))

#Q-Q plot
qqnorm(resid(Tol_Chl_M12_lm)); qqline(resid(Tol_Chl_M12_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Chl_M12_lm), resid(Tol_Chl_M12_lm))

```


### Model Results

#### Overall
```{r}
#Model Results
summary(Tol_Chl_M12_lm)
anova(Tol_Chl_M12_lm)

#Effect Size of Predictors
eta_squared(Tol_Chl_M12_lm, partial=FALSE)

##Save model results
Tol_Chl_M12_lm.res<-data.frame(anova(Tol_Chl_M12_lm))
Tol_Chl_M12_lm.res$Predictor<-rownames(Tol_Chl_M12_lm.res)
Tol_Chl_M12_lm.res$EtaSq<-c(eta_squared(Tol_Chl_M12_lm, partial=FALSE)$Eta2, NA)
Tol_Chl_M12_lm.res$Response<-rep("Chlorophyll", nrow(Tol_Chl_M12_lm.res))
Tol_Chl_M12_lm.res$TimeP<-rep("M12", nrow(Tol_Chl_M12_lm.res))
Tol_Chl_M12_lm.res<-Tol_Chl_M12_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```

No significant effects. Still checking Site*Genotype for comparability across Timepoints.


#### Pairwise
```{r}
#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_Chl_M12_lm, pairwise~Genotype | Site)

#Sites within Genotypes
emmeans(Tol_Chl_M12_lm, pairwise~Site | Genotype)

##Save p-values

#Genotypes within Sites
Tol_Chl_M12_lm.geno<-data.frame(emmeans(Tol_Chl_M12_lm, pairwise~Genotype | Site)$contrasts)
Tol_Chl_M12_lm.geno<-Tol_Chl_M12_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_M12_lm.geno$group1<-paste(Tol_Chl_M12_lm.geno$Site, Tol_Chl_M12_lm.geno$group1, sep="_")
Tol_Chl_M12_lm.geno$group2<-paste(Tol_Chl_M12_lm.geno$Site, Tol_Chl_M12_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_Chl_M12_lm.site<-data.frame(emmeans(Tol_Chl_M12_lm, pairwise~Site | Genotype)$contrasts)
Tol_Chl_M12_lm.site<-Tol_Chl_M12_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Chl_M12_lm.site$group1<-paste(Tol_Chl_M12_lm.site$group1, Tol_Chl_M12_lm.site$Genotype, sep="_")
Tol_Chl_M12_lm.site$group2<-paste(Tol_Chl_M12_lm.site$group2, Tol_Chl_M12_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_Chl_M12_lm.p<-rbind(Tol_Chl_M12_lm.geno[,c(1:2,4:8)], Tol_Chl_M12_lm.site[,c(1:2,4:8)])
Tol_Chl_M12_lm.p<-Tol_Chl_M12_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_Chl_M12_lm.p$Sig<-ifelse(Tol_Chl_M12_lm.p$p<0.001, "***", ifelse(Tol_Chl_M12_lm.p$p<0.01, "**", ifelse(Tol_Chl_M12_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_Chl_M12_lm.p$Response<-rep("Chlorophyll", nrow(Tol_Chl_M12_lm.p))
Tol_Chl_M12_lm.p$TimeP<-rep("M12", nrow(Tol_Chl_M12_lm.p))

```


### Plot Retention by Site and Genotype
```{r}
##Summary statistics by Site and Genotype
Tol_Chl_M12_SG<-summarySE(TolData_M12, measurevar="Chl.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_Chl_M12_SG.plot<-ggplot(Tol_Chl_M12_SG, aes(x=Site.Geno, y=Chl.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Chlorophyll Retention")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o); Tol_Chl_M12_SG.plot

#+ stat_pvalue_manual(data=Tol_Chl_M12_lm.p,  y.position=0.2, step.increase=0.20, label="Sig", hide.ns=TRUE) #No significant differences

```


# Thermal Tolerance Symbionts

## Full Set

### Run Model
```{r}
##Check normality
hist(TolData$Sym.prop)
shapiro.test(TolData$Sym.prop)
#Not Normal

##Try square transformation
hist((TolData$Sym.prop)^2)
shapiro.test((TolData$Sym.prop)^2)
#Not normal 

##Try cubed transformation
hist((TolData$Sym.prop)^3)
shapiro.test((TolData$Sym.prop)^3)
#Not normal 


##Model as a function of Site and Genotype and Timepoint
##Model with no transformation and check residuals
Tol_Sym_lm<-lm(Sym.prop~Site+Genotype+TimeP+ Site:Genotype + Site:TimeP + Genotype:TimeP, data=TolData)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Sym_lm)))

#Q-Q plot
qqnorm(resid(Tol_Sym_lm)); qqline(resid(Tol_Sym_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Sym_lm), resid(Tol_Sym_lm))

```


### Model Results

```{r}
#Model Results
summary(Tol_Sym_lm)
anova(Tol_Sym_lm)

#Effect Size of Predictors
eta_squared(Tol_Sym_lm, partial=FALSE)

##Save model results
Tol_Sym_lm.res<-data.frame(anova(Tol_Sym_lm))
Tol_Sym_lm.res$Predictor<-rownames(Tol_Sym_lm.res)
Tol_Sym_lm.res$EtaSq<-c(eta_squared(Tol_Sym_lm, partial=FALSE)$Eta2, NA)
Tol_Sym_lm.res$Response<-rep("Symbionts", nrow(Tol_Sym_lm.res))
Tol_Sym_lm.res<-Tol_Sym_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```
Strong influence of Timepoint, including interactions with main variables of interest, Site and Genotype. Will analyze each Timepoint individually. 


## Sym Timepoint W2
Symbiont density was not measured at W1, starting with W2 for this response.

### Run Model
```{r}
##Check normality
hist(TolData_W2$Sym.prop)
shapiro.test(TolData_W2$Sym.prop)
#Normal

##Model as a function of Site and Genotype
Tol_Sym_W2_lm<-lm(Sym.prop~Site+Genotype+Site:Genotype, data=TolData_W2)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Sym_W2_lm)))

#Q-Q plot
qqnorm(resid(Tol_Sym_W2_lm)); qqline(resid(Tol_Sym_W2_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Sym_W2_lm), resid(Tol_Sym_W2_lm))

```


### Model Results

#### Overall
```{r}
#Model Results
summary(Tol_Sym_W2_lm)
anova(Tol_Sym_W2_lm)

#Effect Size of Predictors
eta_squared(Tol_Sym_W2_lm, partial=FALSE)

##Save model results
Tol_Sym_W2_lm.res<-data.frame(anova(Tol_Sym_W2_lm))
Tol_Sym_W2_lm.res$Predictor<-rownames(Tol_Sym_W2_lm.res)
Tol_Sym_W2_lm.res$EtaSq<-c(eta_squared(Tol_Sym_W2_lm, partial=FALSE)$Eta2, NA)
Tol_Sym_W2_lm.res$Response<-rep("Symbionts", nrow(Tol_Sym_W2_lm.res))
Tol_Sym_W2_lm.res$TimeP<-rep("W2", nrow(Tol_Sym_W2_lm.res))
Tol_Sym_W2_lm.res<-Tol_Sym_W2_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```

Significant effect of Site and Genotype. Marginal effect (p<0.1) of Site * Genotype. Still checking Site*Genotype for comparability across Timepoints.



#### Pairwise
```{r}
#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_Sym_W2_lm, pairwise~Genotype | Site)

#Sites within Genotypes
emmeans(Tol_Sym_W2_lm, pairwise~Site | Genotype)

##Save p-values

#Genotypes within Sites
Tol_Sym_W2_lm.geno<-data.frame(emmeans(Tol_Sym_W2_lm, pairwise~Genotype | Site)$contrasts)
Tol_Sym_W2_lm.geno<-Tol_Sym_W2_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Sym_W2_lm.geno$group1<-paste(Tol_Sym_W2_lm.geno$Site, Tol_Sym_W2_lm.geno$group1, sep="_")
Tol_Sym_W2_lm.geno$group2<-paste(Tol_Sym_W2_lm.geno$Site, Tol_Sym_W2_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_Sym_W2_lm.site<-data.frame(emmeans(Tol_Sym_W2_lm, pairwise~Site | Genotype)$contrasts)
Tol_Sym_W2_lm.site<-Tol_Sym_W2_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Sym_W2_lm.site$group1<-paste(Tol_Sym_W2_lm.site$group1, Tol_Sym_W2_lm.site$Genotype, sep="_")
Tol_Sym_W2_lm.site$group2<-paste(Tol_Sym_W2_lm.site$group2, Tol_Sym_W2_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_Sym_W2_lm.p<-rbind(Tol_Sym_W2_lm.geno[,c(1:2,4:8)], Tol_Sym_W2_lm.site[,c(1:2,4:8)])
Tol_Sym_W2_lm.p<-Tol_Sym_W2_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_Sym_W2_lm.p$Sig<-ifelse(Tol_Sym_W2_lm.p$p<0.001, "***", ifelse(Tol_Sym_W2_lm.p$p<0.01, "**", ifelse(Tol_Sym_W2_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_Sym_W2_lm.p$Response<-rep("Symbionts", nrow(Tol_Sym_W2_lm.p))
Tol_Sym_W2_lm.p$TimeP<-rep("W2", nrow(Tol_Sym_W2_lm.p))

```


### Plot Retention by Site and Genotype
```{r}
##Summary statistics by Site and Genotype
Tol_Sym_W2_SG<-summarySE(TolData_W2, measurevar="Sym.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_Sym_W2_SG.plot<-ggplot(Tol_Sym_W2_SG, aes(x=Site.Geno, y=Sym.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Symbiont Retention")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_Sym_W2_lm.p,  y.position=0.95, step.increase=0.15, label="Sig", hide.ns=TRUE); Tol_Sym_W2_SG.plot

```


## Sym Timepoint M1

### Run Model
```{r}
##Check normality
hist(TolData_M1$Sym.prop)
shapiro.test(TolData_M1$Sym.prop)
#Not normal

##Try log+1 transformation
hist(log(TolData_M1$Sym.prop+1))
shapiro.test(log(TolData_M1$Sym.prop+1))
#Not normal but improved

##Model as a function of Site and Genotype
##Model with log transformation
Tol_Sym_M1_lm<-lm(log(Sym.prop+1)~Site+Genotype+Site:Genotype, data=TolData_M1)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Sym_M1_lm)))

#Q-Q plot
qqnorm(resid(Tol_Sym_M1_lm)); qqline(resid(Tol_Sym_M1_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Sym_M1_lm), resid(Tol_Sym_M1_lm))

```


### Model Results

#### Overall
```{r}
#Model Results
summary(Tol_Sym_M1_lm)
anova(Tol_Sym_M1_lm)

#Effect Size of Predictors
eta_squared(Tol_Sym_M1_lm, partial=FALSE)

##Save model results
Tol_Sym_M1_lm.res<-data.frame(anova(Tol_Sym_M1_lm))
Tol_Sym_M1_lm.res$Predictor<-rownames(Tol_Sym_M1_lm.res)
Tol_Sym_M1_lm.res$EtaSq<-c(eta_squared(Tol_Sym_M1_lm, partial=FALSE)$Eta2, NA)
Tol_Sym_M1_lm.res$Response<-rep("Symbionts", nrow(Tol_Sym_M1_lm.res))
Tol_Sym_M1_lm.res$TimeP<-rep("M1", nrow(Tol_Sym_M1_lm.res))
Tol_Sym_M1_lm.res<-Tol_Sym_M1_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```

Significant effect of Genotype. Still checking Site*Genotype for comparability across Timepoints.


#### Pairwise
```{r}
#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_Sym_M1_lm, pairwise~Genotype | Site)

#Sites within Genotypes
emmeans(Tol_Sym_M1_lm, pairwise~Site | Genotype)

##Save p-values

#Genotypes within Sites
Tol_Sym_M1_lm.geno<-data.frame(emmeans(Tol_Sym_M1_lm, pairwise~Genotype | Site)$contrasts)
Tol_Sym_M1_lm.geno<-Tol_Sym_M1_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Sym_M1_lm.geno$group1<-paste(Tol_Sym_M1_lm.geno$Site, Tol_Sym_M1_lm.geno$group1, sep="_")
Tol_Sym_M1_lm.geno$group2<-paste(Tol_Sym_M1_lm.geno$Site, Tol_Sym_M1_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_Sym_M1_lm.site<-data.frame(emmeans(Tol_Sym_M1_lm, pairwise~Site | Genotype)$contrasts)
Tol_Sym_M1_lm.site<-Tol_Sym_M1_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Sym_M1_lm.site$group1<-paste(Tol_Sym_M1_lm.site$group1, Tol_Sym_M1_lm.site$Genotype, sep="_")
Tol_Sym_M1_lm.site$group2<-paste(Tol_Sym_M1_lm.site$group2, Tol_Sym_M1_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_Sym_M1_lm.p<-rbind(Tol_Sym_M1_lm.geno[,c(1:2,4:8)], Tol_Sym_M1_lm.site[,c(1:2,4:8)])
Tol_Sym_M1_lm.p<-Tol_Sym_M1_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_Sym_M1_lm.p$Sig<-ifelse(Tol_Sym_M1_lm.p$p<0.001, "***", ifelse(Tol_Sym_M1_lm.p$p<0.01, "**", ifelse(Tol_Sym_M1_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_Sym_M1_lm.p$Response<-rep("Symbionts", nrow(Tol_Sym_M1_lm.p))
Tol_Sym_M1_lm.p$TimeP<-rep("M1", nrow(Tol_Sym_M1_lm.p))

```


### Plot Retention by Site and Genotype
```{r}
##Summary statistics by Site and Genotype
Tol_Sym_M1_SG<-summarySE(TolData_M1, measurevar="Sym.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_Sym_M1_SG.plot<-ggplot(Tol_Sym_M1_SG, aes(x=Site.Geno, y=Sym.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Symbiont Retention")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
 ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_Sym_M1_lm.p,  y.position=0.95, step.increase=0.15, label="Sig", hide.ns=TRUE); Tol_Sym_M1_SG.plot

```


## Sym Timepoint M4

### Run Model
```{r}
##Check normality
hist(TolData_M4$Sym.prop)
shapiro.test(TolData_M4$Sym.prop)
#Not Normal

##Try square transformation
hist((TolData_M4$Sym.prop)^2)
shapiro.test((TolData_M4$Sym.prop)^2)
#Not Normal

##Try cubed transformation
hist((TolData_M4$Sym.prop)^3)
shapiro.test((TolData_M4$Sym.prop)^3)
#Not Normal

##Model as a function of Site and Genotype
##Model with no transformation and check residuals
Tol_Sym_M4_lm<-lm(Sym.prop~Site+Genotype+Site:Genotype, data=TolData_M4)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Sym_M4_lm)))

#Q-Q plot
qqnorm(resid(Tol_Sym_M4_lm)); qqline(resid(Tol_Sym_M4_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Sym_M4_lm), resid(Tol_Sym_M4_lm))

```


### Model Results

#### Overall
```{r}
#Model Results
summary(Tol_Sym_M4_lm)
anova(Tol_Sym_M4_lm)

#Effect Size of Predictors
eta_squared(Tol_Sym_M4_lm, partial=FALSE)

##Save model results
Tol_Sym_M4_lm.res<-data.frame(anova(Tol_Sym_M4_lm))
Tol_Sym_M4_lm.res$Predictor<-rownames(Tol_Sym_M4_lm.res)
Tol_Sym_M4_lm.res$EtaSq<-c(eta_squared(Tol_Sym_M4_lm, partial=FALSE)$Eta2, NA)
Tol_Sym_M4_lm.res$Response<-rep("Symbionts", nrow(Tol_Sym_M4_lm.res))
Tol_Sym_M4_lm.res$TimeP<-rep("M4", nrow(Tol_Sym_M4_lm.res))
Tol_Sym_M4_lm.res<-Tol_Sym_M4_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```

Significant effect of Site and Genotype. Marginal effect (p<0.1) of Site * Genotype. Still checking Site*Genotype for comparability across Timepoints.



#### Pairwise
```{r}
#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_Sym_M4_lm, pairwise~Genotype | Site)

#Sites within Genotypes
emmeans(Tol_Sym_M4_lm, pairwise~Site | Genotype)

##Save p-values

#Genotypes within Sites
Tol_Sym_M4_lm.geno<-data.frame(emmeans(Tol_Sym_M4_lm, pairwise~Genotype | Site)$contrasts)
Tol_Sym_M4_lm.geno<-Tol_Sym_M4_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Sym_M4_lm.geno$group1<-paste(Tol_Sym_M4_lm.geno$Site, Tol_Sym_M4_lm.geno$group1, sep="_")
Tol_Sym_M4_lm.geno$group2<-paste(Tol_Sym_M4_lm.geno$Site, Tol_Sym_M4_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_Sym_M4_lm.site<-data.frame(emmeans(Tol_Sym_M4_lm, pairwise~Site | Genotype)$contrasts)
Tol_Sym_M4_lm.site<-Tol_Sym_M4_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Sym_M4_lm.site$group1<-paste(Tol_Sym_M4_lm.site$group1, Tol_Sym_M4_lm.site$Genotype, sep="_")
Tol_Sym_M4_lm.site$group2<-paste(Tol_Sym_M4_lm.site$group2, Tol_Sym_M4_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_Sym_M4_lm.p<-rbind(Tol_Sym_M4_lm.geno[,c(1:2,4:8)], Tol_Sym_M4_lm.site[,c(1:2,4:8)])
Tol_Sym_M4_lm.p<-Tol_Sym_M4_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_Sym_M4_lm.p$Sig<-ifelse(Tol_Sym_M4_lm.p$p<0.001, "***", ifelse(Tol_Sym_M4_lm.p$p<0.01, "**", ifelse(Tol_Sym_M4_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_Sym_M4_lm.p$Response<-rep("Symbionts", nrow(Tol_Sym_M4_lm.p))
Tol_Sym_M4_lm.p$TimeP<-rep("M4", nrow(Tol_Sym_M4_lm.p))

```


### Plot Retention by Site and Genotype
```{r}
##Summary statistics by Site and Genotype
Tol_Sym_M4_SG<-summarySE(TolData_M4, measurevar="Sym.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_Sym_M4_SG.plot<-ggplot(Tol_Sym_M4_SG, aes(x=Site.Geno, y=Sym.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Symbiont Retention")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_Sym_M4_lm.p,  y.position=1.1, step.increase=0.15, label="Sig", hide.ns=TRUE); Tol_Sym_M4_SG.plot

```


## Sym Timepoint M8

### Run Model
```{r}
##Check normality
hist(TolData_M8$Sym.prop)
shapiro.test(TolData_M8$Sym.prop)
#Not Normal

##Try square transformation
hist((TolData_M8$Sym.prop)^2)
shapiro.test((TolData_M8$Sym.prop)^2)
#Not Normal

##Try cubed transformation
hist((TolData_M8$Sym.prop)^3)
shapiro.test((TolData_M8$Sym.prop)^3)
#Not Normal

##Model as a function of Site and Genotype
##Model with no transformation and check residuals
Tol_Sym_M8_lm<-lm(Sym.prop~Site+Genotype+Site:Genotype, data=TolData_M8)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Sym_M8_lm)))

#Q-Q plot
qqnorm(resid(Tol_Sym_M8_lm)); qqline(resid(Tol_Sym_M8_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Sym_M8_lm), resid(Tol_Sym_M8_lm))

```


### Model Results

#### Overall
```{r}
#Model Results
summary(Tol_Sym_M8_lm)
anova(Tol_Sym_M8_lm)

#Effect Size of Predictors
eta_squared(Tol_Sym_M8_lm, partial=FALSE)

##Save model results
Tol_Sym_M8_lm.res<-data.frame(anova(Tol_Sym_M8_lm))
Tol_Sym_M8_lm.res$Predictor<-rownames(Tol_Sym_M8_lm.res)
Tol_Sym_M8_lm.res$EtaSq<-c(eta_squared(Tol_Sym_M8_lm, partial=FALSE)$Eta2, NA)
Tol_Sym_M8_lm.res$Response<-rep("Symbionts", nrow(Tol_Sym_M8_lm.res))
Tol_Sym_M8_lm.res$TimeP<-rep("M8", nrow(Tol_Sym_M8_lm.res))
Tol_Sym_M8_lm.res<-Tol_Sym_M8_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```

Significant effect of Site. Marginal effect (p<0.1) of Site * Genotype. Still checking Site*Genotype for comparability across Timepoints.


#### Pairwise
```{r}
#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_Sym_M8_lm, pairwise~Genotype | Site)

#Sites within Genotypes
emmeans(Tol_Sym_M8_lm, pairwise~Site | Genotype)

##Save p-values

#Genotypes within Sites
Tol_Sym_M8_lm.geno<-data.frame(emmeans(Tol_Sym_M8_lm, pairwise~Genotype | Site)$contrasts)
Tol_Sym_M8_lm.geno<-Tol_Sym_M8_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Sym_M8_lm.geno$group1<-paste(Tol_Sym_M8_lm.geno$Site, Tol_Sym_M8_lm.geno$group1, sep="_")
Tol_Sym_M8_lm.geno$group2<-paste(Tol_Sym_M8_lm.geno$Site, Tol_Sym_M8_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_Sym_M8_lm.site<-data.frame(emmeans(Tol_Sym_M8_lm, pairwise~Site | Genotype)$contrasts)
Tol_Sym_M8_lm.site<-Tol_Sym_M8_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Sym_M8_lm.site$group1<-paste(Tol_Sym_M8_lm.site$group1, Tol_Sym_M8_lm.site$Genotype, sep="_")
Tol_Sym_M8_lm.site$group2<-paste(Tol_Sym_M8_lm.site$group2, Tol_Sym_M8_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_Sym_M8_lm.p<-rbind(Tol_Sym_M8_lm.geno[,c(1:2,4:8)], Tol_Sym_M8_lm.site[,c(1:2,4:8)])
Tol_Sym_M8_lm.p<-Tol_Sym_M8_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_Sym_M8_lm.p$Sig<-ifelse(Tol_Sym_M8_lm.p$p<0.001, "***", ifelse(Tol_Sym_M8_lm.p$p<0.01, "**", ifelse(Tol_Sym_M8_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_Sym_M8_lm.p$Response<-rep("Symbionts", nrow(Tol_Sym_M8_lm.p))
Tol_Sym_M8_lm.p$TimeP<-rep("M8", nrow(Tol_Sym_M8_lm.p))

```


### Plot Retention by Site and Genotype
```{r}
##Summary statistics by Site and Genotype
Tol_Sym_M8_SG<-summarySE(TolData_M8, measurevar="Sym.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_Sym_M8_SG.plot<-ggplot(Tol_Sym_M8_SG, aes(x=Site.Geno, y=Sym.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Symbiont Retention")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_Sym_M8_lm.p,  y.position=1.05, step.increase=0.4, label="Sig", hide.ns=TRUE); Tol_Sym_M8_SG.plot

```


## Sym Timepoint M12

### Run Model
```{r}
##Check normality
hist(TolData_M12$Sym.prop)
shapiro.test(TolData_M12$Sym.prop)
#Normal

##Model as a function of Site and Genotype
Tol_Sym_M12_lm<-lm(Sym.prop~Site+Genotype+Site:Genotype, data=TolData_M12)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_Sym_M12_lm)))

#Q-Q plot
qqnorm(resid(Tol_Sym_M12_lm)); qqline(resid(Tol_Sym_M12_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_Sym_M12_lm), resid(Tol_Sym_M12_lm))

```


### Model Results

#### Overall
```{r}
#Model Results
summary(Tol_Sym_M12_lm)
anova(Tol_Sym_M12_lm)

#Effect Size of Predictors
eta_squared(Tol_Sym_M12_lm, partial=FALSE)

##Save model results
Tol_Sym_M12_lm.res<-data.frame(anova(Tol_Sym_M12_lm))
Tol_Sym_M12_lm.res$Predictor<-rownames(Tol_Sym_M12_lm.res)
Tol_Sym_M12_lm.res$EtaSq<-c(eta_squared(Tol_Sym_M12_lm, partial=FALSE)$Eta2, NA)
Tol_Sym_M12_lm.res$Response<-rep("Symbionts", nrow(Tol_Sym_M12_lm.res))
Tol_Sym_M12_lm.res$TimeP<-rep("M12", nrow(Tol_Sym_M12_lm.res))
Tol_Sym_M12_lm.res<-Tol_Sym_M12_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```

No significant effects of Site or Genotype. Still checking Site*Genotype for comparability across Timepoints.


#### Pairwise
```{r}
#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_Sym_M12_lm, pairwise~Genotype | Site)

#Sites within Genotypes
emmeans(Tol_Sym_M12_lm, pairwise~Site | Genotype)

##Save p-values

#Genotypes within Sites
Tol_Sym_M12_lm.geno<-data.frame(emmeans(Tol_Sym_M12_lm, pairwise~Genotype | Site)$contrasts)
Tol_Sym_M12_lm.geno<-Tol_Sym_M12_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Sym_M12_lm.geno$group1<-paste(Tol_Sym_M12_lm.geno$Site, Tol_Sym_M12_lm.geno$group1, sep="_")
Tol_Sym_M12_lm.geno$group2<-paste(Tol_Sym_M12_lm.geno$Site, Tol_Sym_M12_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_Sym_M12_lm.site<-data.frame(emmeans(Tol_Sym_M12_lm, pairwise~Site | Genotype)$contrasts)
Tol_Sym_M12_lm.site<-Tol_Sym_M12_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_Sym_M12_lm.site$group1<-paste(Tol_Sym_M12_lm.site$group1, Tol_Sym_M12_lm.site$Genotype, sep="_")
Tol_Sym_M12_lm.site$group2<-paste(Tol_Sym_M12_lm.site$group2, Tol_Sym_M12_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_Sym_M12_lm.p<-rbind(Tol_Sym_M12_lm.geno[,c(1:2,4:8)], Tol_Sym_M12_lm.site[,c(1:2,4:8)])
Tol_Sym_M12_lm.p<-Tol_Sym_M12_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_Sym_M12_lm.p$Sig<-ifelse(Tol_Sym_M12_lm.p$p<0.001, "***", ifelse(Tol_Sym_M12_lm.p$p<0.01, "**", ifelse(Tol_Sym_M12_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_Sym_M12_lm.p$Response<-rep("Symbionts", nrow(Tol_Sym_M12_lm.p))
Tol_Sym_M12_lm.p$TimeP<-rep("M12", nrow(Tol_Sym_M12_lm.p))

```


### Plot Retention by Site and Genotype
```{r}
##Summary statistics by Site and Genotype
Tol_Sym_M12_SG<-summarySE(TolData_M12, measurevar="Sym.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_Sym_M12_SG.plot<-ggplot(Tol_Sym_M12_SG, aes(x=Site.Geno, y=Sym.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Symbiont Retention")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o); Tol_Sym_M12_SG.plot

#+ stat_pvalue_manual(data=Tol_Sym_M12_lm.p,  y.position=0.95, step.increase=0.15, label="Sig", hide.ns=TRUE) #No significant differences
```


# Thermal Tolerance Color Full Set

## Full Set

### Run Model
```{r}
##Check normality
hist(TolData$Score_Full.prop)
shapiro.test(TolData$Score_Full.prop)
#Not Normal

##Try square transformation
hist((TolData$Score_Full.prop)^2)
shapiro.test((TolData$Score_Full.prop)^2)
#Not normal 

##Try cubed transformation
hist((TolData$Score_Full.prop)^3)
shapiro.test((TolData$Score_Full.prop)^3)
#Not normal 


##Model as a function of Site and Genotype and Timepoint
##Model with no transformation and check residuals
Tol_ScoreF_lm<-lm(Score_Full.prop~Site+Genotype+TimeP+ Site:Genotype + Site:TimeP + Genotype:TimeP, data=TolData)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreF_lm)))

#Q-Q plot
qqnorm(resid(Tol_ScoreF_lm)); qqline(resid(Tol_ScoreF_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreF_lm), resid(Tol_ScoreF_lm))

```

Residuals are not great, need to check for other modeling options for Color Full Set.


### Model Results

```{r}
#Model Results
summary(Tol_ScoreF_lm)
anova(Tol_ScoreF_lm)

#Effect Size of Predictors
eta_squared(Tol_ScoreF_lm, partial=FALSE)

##Save model results
Tol_ScoreF_lm.res<-data.frame(anova(Tol_ScoreF_lm))
Tol_ScoreF_lm.res$Predictor<-rownames(Tol_ScoreF_lm.res)
Tol_ScoreF_lm.res$EtaSq<-c(eta_squared(Tol_ScoreF_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreF_lm.res$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_lm.res))
Tol_ScoreF_lm.res<-Tol_ScoreF_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```
Strong influence of Timepoint, including interactions with Genotype. Will analyze each Timepoint individually. 


## Color Score Full Set Timepoint W1

### Run Model
```{r}
##Check normality
hist(TolData_W1$Score_Full.prop)
shapiro.test(TolData_W1$Score_Full.prop)
#Normal

##Model as a function of Site and Genotype
Tol_ScoreF_W1_lm<-lm(Score_Full.prop~Site+Genotype+Site:Genotype, data=TolData_W1)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreF_W1_lm)))

#Q-Q plot
qqnorm(resid(Tol_ScoreF_W1_lm)); qqline(resid(Tol_ScoreF_W1_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreF_W1_lm), resid(Tol_ScoreF_W1_lm))

```


### Model Results

#### Overall
```{r}
#Model Results
summary(Tol_ScoreF_W1_lm)
anova(Tol_ScoreF_W1_lm)

#Effect Size of Predictors
eta_squared(Tol_ScoreF_W1_lm, partial=FALSE)

##Save model results
Tol_ScoreF_W1_lm.res<-data.frame(anova(Tol_ScoreF_W1_lm))
Tol_ScoreF_W1_lm.res$Predictor<-rownames(Tol_ScoreF_W1_lm.res)
Tol_ScoreF_W1_lm.res$EtaSq<-c(eta_squared(Tol_ScoreF_W1_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreF_W1_lm.res$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_W1_lm.res))
Tol_ScoreF_W1_lm.res$TimeP<-rep("W1", nrow(Tol_ScoreF_W1_lm.res))
Tol_ScoreF_W1_lm.res<-Tol_ScoreF_W1_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```

Significant effect of Genotype. Still checking Site*Genotype for comparability across Timepoints.



#### Pairwise
```{r}
#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreF_W1_lm, pairwise~Genotype | Site)

#Sites within Genotypes
emmeans(Tol_ScoreF_W1_lm, pairwise~Site | Genotype)

##Save p-values

#Genotypes within Sites
Tol_ScoreF_W1_lm.geno<-data.frame(emmeans(Tol_ScoreF_W1_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreF_W1_lm.geno<-Tol_ScoreF_W1_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_W1_lm.geno$group1<-paste(Tol_ScoreF_W1_lm.geno$Site, Tol_ScoreF_W1_lm.geno$group1, sep="_")
Tol_ScoreF_W1_lm.geno$group2<-paste(Tol_ScoreF_W1_lm.geno$Site, Tol_ScoreF_W1_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreF_W1_lm.site<-data.frame(emmeans(Tol_ScoreF_W1_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreF_W1_lm.site<-Tol_ScoreF_W1_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_W1_lm.site$group1<-paste(Tol_ScoreF_W1_lm.site$group1, Tol_ScoreF_W1_lm.site$Genotype, sep="_")
Tol_ScoreF_W1_lm.site$group2<-paste(Tol_ScoreF_W1_lm.site$group2, Tol_ScoreF_W1_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreF_W1_lm.p<-rbind(Tol_ScoreF_W1_lm.geno[,c(1:2,4:8)], Tol_ScoreF_W1_lm.site[,c(1:2,4:8)])
Tol_ScoreF_W1_lm.p<-Tol_ScoreF_W1_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreF_W1_lm.p$Sig<-ifelse(Tol_ScoreF_W1_lm.p$p<0.001, "***", ifelse(Tol_ScoreF_W1_lm.p$p<0.01, "**", ifelse(Tol_ScoreF_W1_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreF_W1_lm.p$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_W1_lm.p))
Tol_ScoreF_W1_lm.p$TimeP<-rep("W1", nrow(Tol_ScoreF_W1_lm.p))

```


### Plot Retention by Site and Genotype
```{r}
##Summary statistics by Site and Genotype
Tol_ScoreF_W1_SG<-summarySE(TolData_W1, measurevar="Score_Full.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreF_W1_SG.plot<-ggplot(Tol_ScoreF_W1_SG, aes(x=Site.Geno, y=Score_Full.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_Full.prop-se, ymax=Score_Full.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention Full Set")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_ScoreF_W1_lm.p,  y.position=0.80, step.increase=0.2, label="Sig", hide.ns=TRUE); Tol_ScoreF_W1_SG.plot

```


## Color Score Full Set Timepoint W2

### Run Model
```{r}
##Check normality
hist(TolData_W2$Score_Full.prop)
shapiro.test(TolData_W2$Score_Full.prop)
#Normal

##Model as a function of Site and Genotype
Tol_ScoreF_W2_lm<-lm(Score_Full.prop~Site+Genotype+Site:Genotype, data=TolData_W2)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreF_W2_lm)))

#Q-Q plot
qqnorm(resid(Tol_ScoreF_W2_lm)); qqline(resid(Tol_ScoreF_W2_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreF_W2_lm), resid(Tol_ScoreF_W2_lm))

```


### Model Results

#### Overall
```{r}
#Model Results
summary(Tol_ScoreF_W2_lm)
anova(Tol_ScoreF_W2_lm)

#Effect Size of Predictors
eta_squared(Tol_ScoreF_W2_lm, partial=FALSE)

##Save model results
Tol_ScoreF_W2_lm.res<-data.frame(anova(Tol_ScoreF_W2_lm))
Tol_ScoreF_W2_lm.res$Predictor<-rownames(Tol_ScoreF_W2_lm.res)
Tol_ScoreF_W2_lm.res$EtaSq<-c(eta_squared(Tol_ScoreF_W2_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreF_W2_lm.res$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_W2_lm.res))
Tol_ScoreF_W2_lm.res$TimeP<-rep("W2", nrow(Tol_ScoreF_W2_lm.res))
Tol_ScoreF_W2_lm.res<-Tol_ScoreF_W2_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```

Significant effect of Site and Genotype. Still checking Site*Genotype for comparability across Timepoints.



#### Pairwise
```{r}
#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreF_W2_lm, pairwise~Genotype | Site)

#Sites within Genotypes
emmeans(Tol_ScoreF_W2_lm, pairwise~Site | Genotype)

##Save p-values

#Genotypes within Sites
Tol_ScoreF_W2_lm.geno<-data.frame(emmeans(Tol_ScoreF_W2_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreF_W2_lm.geno<-Tol_ScoreF_W2_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_W2_lm.geno$group1<-paste(Tol_ScoreF_W2_lm.geno$Site, Tol_ScoreF_W2_lm.geno$group1, sep="_")
Tol_ScoreF_W2_lm.geno$group2<-paste(Tol_ScoreF_W2_lm.geno$Site, Tol_ScoreF_W2_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreF_W2_lm.site<-data.frame(emmeans(Tol_ScoreF_W2_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreF_W2_lm.site<-Tol_ScoreF_W2_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_W2_lm.site$group1<-paste(Tol_ScoreF_W2_lm.site$group1, Tol_ScoreF_W2_lm.site$Genotype, sep="_")
Tol_ScoreF_W2_lm.site$group2<-paste(Tol_ScoreF_W2_lm.site$group2, Tol_ScoreF_W2_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreF_W2_lm.p<-rbind(Tol_ScoreF_W2_lm.geno[,c(1:2,4:8)], Tol_ScoreF_W2_lm.site[,c(1:2,4:8)])
Tol_ScoreF_W2_lm.p<-Tol_ScoreF_W2_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreF_W2_lm.p$Sig<-ifelse(Tol_ScoreF_W2_lm.p$p<0.001, "***", ifelse(Tol_ScoreF_W2_lm.p$p<0.01, "**", ifelse(Tol_ScoreF_W2_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreF_W2_lm.p$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_W2_lm.p))
Tol_ScoreF_W2_lm.p$TimeP<-rep("W2", nrow(Tol_ScoreF_W2_lm.p))

```


### Plot Retention by Site and Genotype
```{r}
##Summary statistics by Site and Genotype
Tol_ScoreF_W2_SG<-summarySE(TolData_W2, measurevar="Score_Full.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreF_W2_SG.plot<-ggplot(Tol_ScoreF_W2_SG, aes(x=Site.Geno, y=Score_Full.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_Full.prop-se, ymax=Score_Full.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention Full Set")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_ScoreF_W2_lm.p,  y.position=0.8, step.increase=0.2, label="Sig", hide.ns=TRUE); Tol_ScoreF_W2_SG.plot

```


## Color Score Full Set Timepoint M1

### Run Model
```{r}
##Check normality
hist(TolData_M1$Score_Full.prop)
shapiro.test(TolData_M1$Score_Full.prop)
#Not normal

##Try square transformation
hist((TolData_M1$Score_Full.prop)^2)
shapiro.test((TolData_M1$Score_Full.prop)^2)
#Not normal

##Try cubed transformation
hist((TolData_M1$Score_Full.prop)^3)
shapiro.test((TolData_M1$Score_Full.prop)^3)
#Not normal

##Model as a function of Site and Genotype
##Model with no transformation and check residuals
Tol_ScoreF_M1_lm<-lm(Score_Full.prop~Site+Genotype+Site:Genotype, data=TolData_M1)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreF_M1_lm)))

#Q-Q plot
qqnorm(resid(Tol_ScoreF_M1_lm)); qqline(resid(Tol_ScoreF_M1_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreF_M1_lm), resid(Tol_ScoreF_M1_lm))

```


### Model Results

#### Overall
```{r}
#Model Results
summary(Tol_ScoreF_M1_lm)
anova(Tol_ScoreF_M1_lm)

#Effect Size of Predictors
eta_squared(Tol_ScoreF_M1_lm, partial=FALSE)

##Save model results
Tol_ScoreF_M1_lm.res<-data.frame(anova(Tol_ScoreF_M1_lm))
Tol_ScoreF_M1_lm.res$Predictor<-rownames(Tol_ScoreF_M1_lm.res)
Tol_ScoreF_M1_lm.res$EtaSq<-c(eta_squared(Tol_ScoreF_M1_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreF_M1_lm.res$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_M1_lm.res))
Tol_ScoreF_M1_lm.res$TimeP<-rep("M1", nrow(Tol_ScoreF_M1_lm.res))
Tol_ScoreF_M1_lm.res<-Tol_ScoreF_M1_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```

Significant effect of Site and Genotype. Still checking Site*Genotype for comparability across Timepoints.


#### Pairwise
```{r}
#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreF_M1_lm, pairwise~Genotype | Site)

#Sites within Genotypes
emmeans(Tol_ScoreF_M1_lm, pairwise~Site | Genotype)

##Save p-values

#Genotypes within Sites
Tol_ScoreF_M1_lm.geno<-data.frame(emmeans(Tol_ScoreF_M1_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreF_M1_lm.geno<-Tol_ScoreF_M1_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_M1_lm.geno$group1<-paste(Tol_ScoreF_M1_lm.geno$Site, Tol_ScoreF_M1_lm.geno$group1, sep="_")
Tol_ScoreF_M1_lm.geno$group2<-paste(Tol_ScoreF_M1_lm.geno$Site, Tol_ScoreF_M1_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreF_M1_lm.site<-data.frame(emmeans(Tol_ScoreF_M1_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreF_M1_lm.site<-Tol_ScoreF_M1_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_M1_lm.site$group1<-paste(Tol_ScoreF_M1_lm.site$group1, Tol_ScoreF_M1_lm.site$Genotype, sep="_")
Tol_ScoreF_M1_lm.site$group2<-paste(Tol_ScoreF_M1_lm.site$group2, Tol_ScoreF_M1_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreF_M1_lm.p<-rbind(Tol_ScoreF_M1_lm.geno[,c(1:2,4:8)], Tol_ScoreF_M1_lm.site[,c(1:2,4:8)])
Tol_ScoreF_M1_lm.p<-Tol_ScoreF_M1_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreF_M1_lm.p$Sig<-ifelse(Tol_ScoreF_M1_lm.p$p<0.001, "***", ifelse(Tol_ScoreF_M1_lm.p$p<0.01, "**", ifelse(Tol_ScoreF_M1_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreF_M1_lm.p$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_M1_lm.p))
Tol_ScoreF_M1_lm.p$TimeP<-rep("M1", nrow(Tol_ScoreF_M1_lm.p))

```


### Plot Retention by Site and Genotype
```{r}
##Summary statistics by Site and Genotype
Tol_ScoreF_M1_SG<-summarySE(TolData_M1, measurevar="Score_Full.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreF_M1_SG.plot<-ggplot(Tol_ScoreF_M1_SG, aes(x=Site.Geno, y=Score_Full.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_Full.prop-se, ymax=Score_Full.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention Full Set")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
 ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_ScoreF_M1_lm.p,  y.position=1.1, step.increase=0.2, label="Sig", hide.ns=TRUE); Tol_ScoreF_M1_SG.plot

```


## Color Score Full Set Timepoint M4

### Run Model
```{r}
##Check normality
hist(TolData_M4$Score_Full.prop)
shapiro.test(TolData_M4$Score_Full.prop)
#Not Normal

##Try square transformation
hist((TolData_M4$Score_Full.prop)^2)
shapiro.test((TolData_M4$Score_Full.prop)^2)
#Not Normal

##Try cubed transformation
hist((TolData_M4$Score_Full.prop)^3)
shapiro.test((TolData_M4$Score_Full.prop)^3)
#Not Normal

##Model as a function of Site and Genotype
##Model with no transformation and check residuals
Tol_ScoreF_M4_lm<-lm(Score_Full.prop~Site+Genotype+Site:Genotype, data=TolData_M4)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreF_M4_lm)))

#Q-Q plot
qqnorm(resid(Tol_ScoreF_M4_lm)); qqline(resid(Tol_ScoreF_M4_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreF_M4_lm), resid(Tol_ScoreF_M4_lm))

```


### Model Results

#### Overall
```{r}
#Model Results
summary(Tol_ScoreF_M4_lm)
anova(Tol_ScoreF_M4_lm)

#Effect Size of Predictors
eta_squared(Tol_ScoreF_M4_lm, partial=FALSE)

##Save model results
Tol_ScoreF_M4_lm.res<-data.frame(anova(Tol_ScoreF_M4_lm))
Tol_ScoreF_M4_lm.res$Predictor<-rownames(Tol_ScoreF_M4_lm.res)
Tol_ScoreF_M4_lm.res$EtaSq<-c(eta_squared(Tol_ScoreF_M4_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreF_M4_lm.res$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_M4_lm.res))
Tol_ScoreF_M4_lm.res$TimeP<-rep("M4", nrow(Tol_ScoreF_M4_lm.res))
Tol_ScoreF_M4_lm.res<-Tol_ScoreF_M4_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```

Significant effect of Site and Genotype. Still checking Site*Genotype for comparability across Timepoints.



#### Pairwise
```{r}
#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreF_M4_lm, pairwise~Genotype | Site)

#Sites within Genotypes
emmeans(Tol_ScoreF_M4_lm, pairwise~Site | Genotype)

##Save p-values

#Genotypes within Sites
Tol_ScoreF_M4_lm.geno<-data.frame(emmeans(Tol_ScoreF_M4_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreF_M4_lm.geno<-Tol_ScoreF_M4_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_M4_lm.geno$group1<-paste(Tol_ScoreF_M4_lm.geno$Site, Tol_ScoreF_M4_lm.geno$group1, sep="_")
Tol_ScoreF_M4_lm.geno$group2<-paste(Tol_ScoreF_M4_lm.geno$Site, Tol_ScoreF_M4_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreF_M4_lm.site<-data.frame(emmeans(Tol_ScoreF_M4_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreF_M4_lm.site<-Tol_ScoreF_M4_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_M4_lm.site$group1<-paste(Tol_ScoreF_M4_lm.site$group1, Tol_ScoreF_M4_lm.site$Genotype, sep="_")
Tol_ScoreF_M4_lm.site$group2<-paste(Tol_ScoreF_M4_lm.site$group2, Tol_ScoreF_M4_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreF_M4_lm.p<-rbind(Tol_ScoreF_M4_lm.geno[,c(1:2,4:8)], Tol_ScoreF_M4_lm.site[,c(1:2,4:8)])
Tol_ScoreF_M4_lm.p<-Tol_ScoreF_M4_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreF_M4_lm.p$Sig<-ifelse(Tol_ScoreF_M4_lm.p$p<0.001, "***", ifelse(Tol_ScoreF_M4_lm.p$p<0.01, "**", ifelse(Tol_ScoreF_M4_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreF_M4_lm.p$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_M4_lm.p))
Tol_ScoreF_M4_lm.p$TimeP<-rep("M4", nrow(Tol_ScoreF_M4_lm.p))

```


### Plot Retention by Site and Genotype
```{r}
##Summary statistics by Site and Genotype
Tol_ScoreF_M4_SG<-summarySE(TolData_M4, measurevar="Score_Full.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreF_M4_SG.plot<-ggplot(Tol_ScoreF_M4_SG, aes(x=Site.Geno, y=Score_Full.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_Full.prop-se, ymax=Score_Full.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention Full Set")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_ScoreF_M4_lm.p,  y.position=1.1, step.increase=0.22, label="Sig", hide.ns=TRUE); Tol_ScoreF_M4_SG.plot

```


## Color Score Full Set Timepoint M8

### Run Model
```{r}
##Check normality
hist(TolData_M8$Score_Full.prop)
shapiro.test(TolData_M8$Score_Full.prop)
#Not Normal

##Try square transformation
hist((TolData_M8$Score_Full.prop)^2)
shapiro.test((TolData_M8$Score_Full.prop)^2)
#Not Normal

##Try cubed transformation
hist((TolData_M8$Score_Full.prop)^3)
shapiro.test((TolData_M8$Score_Full.prop)^3)
#Not Normal

##Model as a function of Site and Genotype
##Model with no transformation and check residuals
Tol_ScoreF_M8_lm<-lm(Score_Full.prop~Site+Genotype+Site:Genotype, data=TolData_M8)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreF_M8_lm)))

#Q-Q plot
qqnorm(resid(Tol_ScoreF_M8_lm)); qqline(resid(Tol_ScoreF_M8_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreF_M8_lm), resid(Tol_ScoreF_M8_lm))

```


### Model Results

#### Overall
```{r}
#Model Results
summary(Tol_ScoreF_M8_lm)
anova(Tol_ScoreF_M8_lm)

#Effect Size of Predictors
eta_squared(Tol_ScoreF_M8_lm, partial=FALSE)

##Save model results
Tol_ScoreF_M8_lm.res<-data.frame(anova(Tol_ScoreF_M8_lm))
Tol_ScoreF_M8_lm.res$Predictor<-rownames(Tol_ScoreF_M8_lm.res)
Tol_ScoreF_M8_lm.res$EtaSq<-c(eta_squared(Tol_ScoreF_M8_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreF_M8_lm.res$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_M8_lm.res))
Tol_ScoreF_M8_lm.res$TimeP<-rep("M8", nrow(Tol_ScoreF_M8_lm.res))
Tol_ScoreF_M8_lm.res<-Tol_ScoreF_M8_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```

Significant effect of Site, Genotype, and Site * Genotype. 


#### Pairwise
```{r}
#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreF_M8_lm, pairwise~Genotype | Site)

#Sites within Genotypes
emmeans(Tol_ScoreF_M8_lm, pairwise~Site | Genotype)

##Save p-values

#Genotypes within Sites
Tol_ScoreF_M8_lm.geno<-data.frame(emmeans(Tol_ScoreF_M8_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreF_M8_lm.geno<-Tol_ScoreF_M8_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_M8_lm.geno$group1<-paste(Tol_ScoreF_M8_lm.geno$Site, Tol_ScoreF_M8_lm.geno$group1, sep="_")
Tol_ScoreF_M8_lm.geno$group2<-paste(Tol_ScoreF_M8_lm.geno$Site, Tol_ScoreF_M8_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreF_M8_lm.site<-data.frame(emmeans(Tol_ScoreF_M8_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreF_M8_lm.site<-Tol_ScoreF_M8_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_M8_lm.site$group1<-paste(Tol_ScoreF_M8_lm.site$group1, Tol_ScoreF_M8_lm.site$Genotype, sep="_")
Tol_ScoreF_M8_lm.site$group2<-paste(Tol_ScoreF_M8_lm.site$group2, Tol_ScoreF_M8_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreF_M8_lm.p<-rbind(Tol_ScoreF_M8_lm.geno[,c(1:2,4:8)], Tol_ScoreF_M8_lm.site[,c(1:2,4:8)])
Tol_ScoreF_M8_lm.p<-Tol_ScoreF_M8_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreF_M8_lm.p$Sig<-ifelse(Tol_ScoreF_M8_lm.p$p<0.001, "***", ifelse(Tol_ScoreF_M8_lm.p$p<0.01, "**", ifelse(Tol_ScoreF_M8_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreF_M8_lm.p$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_M8_lm.p))
Tol_ScoreF_M8_lm.p$TimeP<-rep("M8", nrow(Tol_ScoreF_M8_lm.p))

```


### Plot Retention by Site and Genotype
```{r}
##Summary statistics by Site and Genotype
Tol_ScoreF_M8_SG<-summarySE(TolData_M8, measurevar="Score_Full.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreF_M8_SG.plot<-ggplot(Tol_ScoreF_M8_SG, aes(x=Site.Geno, y=Score_Full.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_Full.prop-se, ymax=Score_Full.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention Full Set")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_ScoreF_M8_lm.p,  y.position=1.1, step.increase=0.45, label="Sig", hide.ns=TRUE); Tol_ScoreF_M8_SG.plot

```


## Color Score Full Set Timepoint M12

### Run Model
```{r}
##Check normality
hist(TolData_M12$Score_Full.prop)
shapiro.test(TolData_M12$Score_Full.prop)
#Normal

##Model as a function of Site and Genotype
Tol_ScoreF_M12_lm<-lm(Score_Full.prop~Site+Genotype+Site:Genotype, data=TolData_M12)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreF_M12_lm)))

#Q-Q plot
qqnorm(resid(Tol_ScoreF_M12_lm)); qqline(resid(Tol_ScoreF_M12_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreF_M12_lm), resid(Tol_ScoreF_M12_lm))

```


### Model Results

#### Overall
```{r}
#Model Results
summary(Tol_ScoreF_M12_lm)
anova(Tol_ScoreF_M12_lm)

#Effect Size of Predictors
eta_squared(Tol_ScoreF_M12_lm, partial=FALSE)

##Save model results
Tol_ScoreF_M12_lm.res<-data.frame(anova(Tol_ScoreF_M12_lm))
Tol_ScoreF_M12_lm.res$Predictor<-rownames(Tol_ScoreF_M12_lm.res)
Tol_ScoreF_M12_lm.res$EtaSq<-c(eta_squared(Tol_ScoreF_M12_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreF_M12_lm.res$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_M12_lm.res))
Tol_ScoreF_M12_lm.res$TimeP<-rep("M12", nrow(Tol_ScoreF_M12_lm.res))
Tol_ScoreF_M12_lm.res<-Tol_ScoreF_M12_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```

No significant effects of Site or Genotype. Still checking Site*Genotype for comparability across Timepoints.


#### Pairwise
```{r}
#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreF_M12_lm, pairwise~Genotype | Site)

#Sites within Genotypes
emmeans(Tol_ScoreF_M12_lm, pairwise~Site | Genotype)

##Save p-values

#Genotypes within Sites
Tol_ScoreF_M12_lm.geno<-data.frame(emmeans(Tol_ScoreF_M12_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreF_M12_lm.geno<-Tol_ScoreF_M12_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_M12_lm.geno$group1<-paste(Tol_ScoreF_M12_lm.geno$Site, Tol_ScoreF_M12_lm.geno$group1, sep="_")
Tol_ScoreF_M12_lm.geno$group2<-paste(Tol_ScoreF_M12_lm.geno$Site, Tol_ScoreF_M12_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreF_M12_lm.site<-data.frame(emmeans(Tol_ScoreF_M12_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreF_M12_lm.site<-Tol_ScoreF_M12_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreF_M12_lm.site$group1<-paste(Tol_ScoreF_M12_lm.site$group1, Tol_ScoreF_M12_lm.site$Genotype, sep="_")
Tol_ScoreF_M12_lm.site$group2<-paste(Tol_ScoreF_M12_lm.site$group2, Tol_ScoreF_M12_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreF_M12_lm.p<-rbind(Tol_ScoreF_M12_lm.geno[,c(1:2,4:8)], Tol_ScoreF_M12_lm.site[,c(1:2,4:8)])
Tol_ScoreF_M12_lm.p<-Tol_ScoreF_M12_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreF_M12_lm.p$Sig<-ifelse(Tol_ScoreF_M12_lm.p$p<0.001, "***", ifelse(Tol_ScoreF_M12_lm.p$p<0.01, "**", ifelse(Tol_ScoreF_M12_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreF_M12_lm.p$Response<-rep("Color_FullSet", nrow(Tol_ScoreF_M12_lm.p))
Tol_ScoreF_M12_lm.p$TimeP<-rep("M12", nrow(Tol_ScoreF_M12_lm.p))

```


### Plot Retention by Site and Genotype
```{r}
##Summary statistics by Site and Genotype
Tol_ScoreF_M12_SG<-summarySE(TolData_M12, measurevar="Score_Full.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreF_M12_SG.plot<-ggplot(Tol_ScoreF_M12_SG, aes(x=Site.Geno, y=Score_Full.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_Full.prop-se, ymax=Score_Full.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention Full Set")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o); Tol_ScoreF_M12_SG.plot

#+ stat_pvalue_manual(data=Tol_ScoreF_M12_lm.p,  y.position=0.8, step.increase=0.15, label="Sig", hide.ns=TRUE) #No significant differences

```


# Thermal Tolerance Color by Timepoint

## Full Set

### Run Model
```{r}
##Check normality
hist(TolData$Score_TP.prop)
shapiro.test(TolData$Score_TP.prop)
#Not Normal

##Try square transformation
hist((TolData$Score_TP.prop)^2)
shapiro.test((TolData$Score_TP.prop)^2)
#Not normal 

##Try cubed transformation
hist((TolData$Score_TP.prop)^3)
shapiro.test((TolData$Score_TP.prop)^3)
#Not normal 


##Model as a function of Site and Genotype and Timepoint
##Model with no transformation and check residuals
Tol_ScoreTP_lm<-lm(Score_TP.prop~Site+Genotype+TimeP+ Site:Genotype + Site:TimeP + Genotype:TimeP, data=TolData)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreTP_lm)))

#Q-Q plot
qqnorm(resid(Tol_ScoreTP_lm)); qqline(resid(Tol_ScoreTP_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreTP_lm), resid(Tol_ScoreTP_lm))

```

Residuals are not great, need to check for other modeling options for Color by Timepoint.


### Model Results

```{r}
#Model Results
summary(Tol_ScoreTP_lm)
anova(Tol_ScoreTP_lm)

#Effect Size of Predictors
eta_squared(Tol_ScoreTP_lm, partial=FALSE)

##Save model results
Tol_ScoreTP_lm.res<-data.frame(anova(Tol_ScoreTP_lm))
Tol_ScoreTP_lm.res$Predictor<-rownames(Tol_ScoreTP_lm.res)
Tol_ScoreTP_lm.res$EtaSq<-c(eta_squared(Tol_ScoreTP_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreTP_lm.res$Response<-rep("Color_TP", nrow(Tol_ScoreTP_lm.res))
Tol_ScoreTP_lm.res<-Tol_ScoreTP_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```
Strong influence of Timepoint, including interactions with variables of interest, Site and Genotype. Will analyze each Timepoint individually. 


## Color Score by Timepoint Timepoint W1

### Run Model
```{r}
##Check normality
hist(TolData_W1$Score_TP.prop)
shapiro.test(TolData_W1$Score_TP.prop)
#Normal

##Model as a function of Site and Genotype
Tol_ScoreTP_W1_lm<-lm(Score_TP.prop~Site+Genotype+Site:Genotype, data=TolData_W1)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreTP_W1_lm)))

#Q-Q plot
qqnorm(resid(Tol_ScoreTP_W1_lm)); qqline(resid(Tol_ScoreTP_W1_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreTP_W1_lm), resid(Tol_ScoreTP_W1_lm))

```


### Model Results

#### Overall
```{r}
#Model Results
summary(Tol_ScoreTP_W1_lm)
anova(Tol_ScoreTP_W1_lm)

#Effect Size of Predictors
eta_squared(Tol_ScoreTP_W1_lm, partial=FALSE)

##Save model results
Tol_ScoreTP_W1_lm.res<-data.frame(anova(Tol_ScoreTP_W1_lm))
Tol_ScoreTP_W1_lm.res$Predictor<-rownames(Tol_ScoreTP_W1_lm.res)
Tol_ScoreTP_W1_lm.res$EtaSq<-c(eta_squared(Tol_ScoreTP_W1_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreTP_W1_lm.res$Response<-rep("Color_TP", nrow(Tol_ScoreTP_W1_lm.res))
Tol_ScoreTP_W1_lm.res$TimeP<-rep("W1", nrow(Tol_ScoreTP_W1_lm.res))
Tol_ScoreTP_W1_lm.res<-Tol_ScoreTP_W1_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```

Significant effect of Genotype. Still checking Site*Genotype for comparability across Timepoints.



#### Pairwise
```{r}
#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreTP_W1_lm, pairwise~Genotype | Site)

#Sites within Genotypes
emmeans(Tol_ScoreTP_W1_lm, pairwise~Site | Genotype)

##Save p-values

#Genotypes within Sites
Tol_ScoreTP_W1_lm.geno<-data.frame(emmeans(Tol_ScoreTP_W1_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreTP_W1_lm.geno<-Tol_ScoreTP_W1_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_W1_lm.geno$group1<-paste(Tol_ScoreTP_W1_lm.geno$Site, Tol_ScoreTP_W1_lm.geno$group1, sep="_")
Tol_ScoreTP_W1_lm.geno$group2<-paste(Tol_ScoreTP_W1_lm.geno$Site, Tol_ScoreTP_W1_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreTP_W1_lm.site<-data.frame(emmeans(Tol_ScoreTP_W1_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreTP_W1_lm.site<-Tol_ScoreTP_W1_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_W1_lm.site$group1<-paste(Tol_ScoreTP_W1_lm.site$group1, Tol_ScoreTP_W1_lm.site$Genotype, sep="_")
Tol_ScoreTP_W1_lm.site$group2<-paste(Tol_ScoreTP_W1_lm.site$group2, Tol_ScoreTP_W1_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreTP_W1_lm.p<-rbind(Tol_ScoreTP_W1_lm.geno[,c(1:2,4:8)], Tol_ScoreTP_W1_lm.site[,c(1:2,4:8)])
Tol_ScoreTP_W1_lm.p<-Tol_ScoreTP_W1_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreTP_W1_lm.p$Sig<-ifelse(Tol_ScoreTP_W1_lm.p$p<0.001, "***", ifelse(Tol_ScoreTP_W1_lm.p$p<0.01, "**", ifelse(Tol_ScoreTP_W1_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreTP_W1_lm.p$Response<-rep("Color_TP", nrow(Tol_ScoreTP_W1_lm.p))
Tol_ScoreTP_W1_lm.p$TimeP<-rep("W1", nrow(Tol_ScoreTP_W1_lm.p))

```


### Plot Retention by Site and Genotype
```{r}
##Summary statistics by Site and Genotype
Tol_ScoreTP_W1_SG<-summarySE(TolData_W1, measurevar="Score_TP.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreTP_W1_SG.plot<-ggplot(Tol_ScoreTP_W1_SG, aes(x=Site.Geno, y=Score_TP.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_TP.prop-se, ymax=Score_TP.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention by Timepoint")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_ScoreTP_W1_lm.p,  y.position=1, step.increase=0.45, label="Sig", hide.ns=TRUE); Tol_ScoreTP_W1_SG.plot

```


## Color Score by Timepoint Timepoint W2

### Run Model
```{r}
##Check normality
hist(TolData_W2$Score_TP.prop)
shapiro.test(TolData_W2$Score_TP.prop)
#Normal

##Model as a function of Site and Genotype
Tol_ScoreTP_W2_lm<-lm(Score_TP.prop~Site+Genotype+Site:Genotype, data=TolData_W2)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreTP_W2_lm)))

#Q-Q plot
qqnorm(resid(Tol_ScoreTP_W2_lm)); qqline(resid(Tol_ScoreTP_W2_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreTP_W2_lm), resid(Tol_ScoreTP_W2_lm))

```


### Model Results

#### Overall
```{r}
#Model Results
summary(Tol_ScoreTP_W2_lm)
anova(Tol_ScoreTP_W2_lm)

#Effect Size of Predictors
eta_squared(Tol_ScoreTP_W2_lm, partial=FALSE)

##Save model results
Tol_ScoreTP_W2_lm.res<-data.frame(anova(Tol_ScoreTP_W2_lm))
Tol_ScoreTP_W2_lm.res$Predictor<-rownames(Tol_ScoreTP_W2_lm.res)
Tol_ScoreTP_W2_lm.res$EtaSq<-c(eta_squared(Tol_ScoreTP_W2_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreTP_W2_lm.res$Response<-rep("Color_TP", nrow(Tol_ScoreTP_W2_lm.res))
Tol_ScoreTP_W2_lm.res$TimeP<-rep("W2", nrow(Tol_ScoreTP_W2_lm.res))
Tol_ScoreTP_W2_lm.res<-Tol_ScoreTP_W2_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```

Significant effect of Site and Genotype. Still checking Site*Genotype for comparability across Timepoints.



#### Pairwise
```{r}
#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreTP_W2_lm, pairwise~Genotype | Site)

#Sites within Genotypes
emmeans(Tol_ScoreTP_W2_lm, pairwise~Site | Genotype)

##Save p-values

#Genotypes within Sites
Tol_ScoreTP_W2_lm.geno<-data.frame(emmeans(Tol_ScoreTP_W2_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreTP_W2_lm.geno<-Tol_ScoreTP_W2_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_W2_lm.geno$group1<-paste(Tol_ScoreTP_W2_lm.geno$Site, Tol_ScoreTP_W2_lm.geno$group1, sep="_")
Tol_ScoreTP_W2_lm.geno$group2<-paste(Tol_ScoreTP_W2_lm.geno$Site, Tol_ScoreTP_W2_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreTP_W2_lm.site<-data.frame(emmeans(Tol_ScoreTP_W2_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreTP_W2_lm.site<-Tol_ScoreTP_W2_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_W2_lm.site$group1<-paste(Tol_ScoreTP_W2_lm.site$group1, Tol_ScoreTP_W2_lm.site$Genotype, sep="_")
Tol_ScoreTP_W2_lm.site$group2<-paste(Tol_ScoreTP_W2_lm.site$group2, Tol_ScoreTP_W2_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreTP_W2_lm.p<-rbind(Tol_ScoreTP_W2_lm.geno[,c(1:2,4:8)], Tol_ScoreTP_W2_lm.site[,c(1:2,4:8)])
Tol_ScoreTP_W2_lm.p<-Tol_ScoreTP_W2_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreTP_W2_lm.p$Sig<-ifelse(Tol_ScoreTP_W2_lm.p$p<0.001, "***", ifelse(Tol_ScoreTP_W2_lm.p$p<0.01, "**", ifelse(Tol_ScoreTP_W2_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreTP_W2_lm.p$Response<-rep("Color_TP", nrow(Tol_ScoreTP_W2_lm.p))
Tol_ScoreTP_W2_lm.p$TimeP<-rep("W2", nrow(Tol_ScoreTP_W2_lm.p))

```


### Plot Retention by Site and Genotype
```{r}
##Summary statistics by Site and Genotype
Tol_ScoreTP_W2_SG<-summarySE(TolData_W2, measurevar="Score_TP.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreTP_W2_SG.plot<-ggplot(Tol_ScoreTP_W2_SG, aes(x=Site.Geno, y=Score_TP.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_TP.prop-se, ymax=Score_TP.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention by Timepoint")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_ScoreTP_W2_lm.p,  y.position=1, step.increase=0.45, label="Sig", hide.ns=TRUE); Tol_ScoreTP_W2_SG.plot

```


## Color Score by Timepoint Timepoint M1

### Run Model
```{r}
##Check normality
hist(TolData_M1$Score_TP.prop)
shapiro.test(TolData_M1$Score_TP.prop)
#Not normal

##Try square transformation
hist((TolData_M1$Score_TP.prop)^2)
shapiro.test((TolData_M1$Score_TP.prop)^2)
#Not normal

##Try cubed transformation
hist((TolData_M1$Score_TP.prop)^3)
shapiro.test((TolData_M1$Score_TP.prop)^3)
#Not normal

##Model as a function of Site and Genotype
##Model with no transformation and check residuals
Tol_ScoreTP_M1_lm<-lm(Score_TP.prop~Site+Genotype+Site:Genotype, data=TolData_M1)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreTP_M1_lm)))

#Q-Q plot
qqnorm(resid(Tol_ScoreTP_M1_lm)); qqline(resid(Tol_ScoreTP_M1_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreTP_M1_lm), resid(Tol_ScoreTP_M1_lm))

```


### Model Results

#### Overall
```{r}
#Model Results
summary(Tol_ScoreTP_M1_lm)
anova(Tol_ScoreTP_M1_lm)

#Effect Size of Predictors
eta_squared(Tol_ScoreTP_M1_lm, partial=FALSE)

##Save model results
Tol_ScoreTP_M1_lm.res<-data.frame(anova(Tol_ScoreTP_M1_lm))
Tol_ScoreTP_M1_lm.res$Predictor<-rownames(Tol_ScoreTP_M1_lm.res)
Tol_ScoreTP_M1_lm.res$EtaSq<-c(eta_squared(Tol_ScoreTP_M1_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreTP_M1_lm.res$Response<-rep("Color_TP", nrow(Tol_ScoreTP_M1_lm.res))
Tol_ScoreTP_M1_lm.res$TimeP<-rep("M1", nrow(Tol_ScoreTP_M1_lm.res))
Tol_ScoreTP_M1_lm.res<-Tol_ScoreTP_M1_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```

Significant effect of Site and Genotype. Still checking Site*Genotype for comparability across Timepoints.


#### Pairwise
```{r}
#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreTP_M1_lm, pairwise~Genotype | Site)

#Sites within Genotypes
emmeans(Tol_ScoreTP_M1_lm, pairwise~Site | Genotype)

##Save p-values

#Genotypes within Sites
Tol_ScoreTP_M1_lm.geno<-data.frame(emmeans(Tol_ScoreTP_M1_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreTP_M1_lm.geno<-Tol_ScoreTP_M1_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_M1_lm.geno$group1<-paste(Tol_ScoreTP_M1_lm.geno$Site, Tol_ScoreTP_M1_lm.geno$group1, sep="_")
Tol_ScoreTP_M1_lm.geno$group2<-paste(Tol_ScoreTP_M1_lm.geno$Site, Tol_ScoreTP_M1_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreTP_M1_lm.site<-data.frame(emmeans(Tol_ScoreTP_M1_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreTP_M1_lm.site<-Tol_ScoreTP_M1_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_M1_lm.site$group1<-paste(Tol_ScoreTP_M1_lm.site$group1, Tol_ScoreTP_M1_lm.site$Genotype, sep="_")
Tol_ScoreTP_M1_lm.site$group2<-paste(Tol_ScoreTP_M1_lm.site$group2, Tol_ScoreTP_M1_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreTP_M1_lm.p<-rbind(Tol_ScoreTP_M1_lm.geno[,c(1:2,4:8)], Tol_ScoreTP_M1_lm.site[,c(1:2,4:8)])
Tol_ScoreTP_M1_lm.p<-Tol_ScoreTP_M1_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreTP_M1_lm.p$Sig<-ifelse(Tol_ScoreTP_M1_lm.p$p<0.001, "***", ifelse(Tol_ScoreTP_M1_lm.p$p<0.01, "**", ifelse(Tol_ScoreTP_M1_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreTP_M1_lm.p$Response<-rep("Color_TP", nrow(Tol_ScoreTP_M1_lm.p))
Tol_ScoreTP_M1_lm.p$TimeP<-rep("M1", nrow(Tol_ScoreTP_M1_lm.p))

```


### Plot Retention by Site and Genotype
```{r}
##Summary statistics by Site and Genotype
Tol_ScoreTP_M1_SG<-summarySE(TolData_M1, measurevar="Score_TP.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreTP_M1_SG.plot<-ggplot(Tol_ScoreTP_M1_SG, aes(x=Site.Geno, y=Score_TP.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_TP.prop-se, ymax=Score_TP.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention by Timepoint")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
 ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_ScoreTP_M1_lm.p,  y.position=1.05, step.increase=0.3, label="Sig", hide.ns=TRUE); Tol_ScoreTP_M1_SG.plot

```


## Color Score by Timepoint Timepoint M4

### Run Model
```{r}
##Check normality
hist(TolData_M4$Score_TP.prop)
shapiro.test(TolData_M4$Score_TP.prop)
#Not Normal

##Try square transformation
hist((TolData_M4$Score_TP.prop)^2)
shapiro.test((TolData_M4$Score_TP.prop)^2)
#Not Normal

##Try cubed transformation
hist((TolData_M4$Score_TP.prop)^3)
shapiro.test((TolData_M4$Score_TP.prop)^3)
#Not Normal

##Model as a function of Site and Genotype
##Model with no transformation and check residuals
Tol_ScoreTP_M4_lm<-lm(Score_TP.prop~Site+Genotype+Site:Genotype, data=TolData_M4)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreTP_M4_lm)))

#Q-Q plot
qqnorm(resid(Tol_ScoreTP_M4_lm)); qqline(resid(Tol_ScoreTP_M4_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreTP_M4_lm), resid(Tol_ScoreTP_M4_lm))

```


### Model Results

#### Overall
```{r}
#Model Results
summary(Tol_ScoreTP_M4_lm)
anova(Tol_ScoreTP_M4_lm)

#Effect Size of Predictors
eta_squared(Tol_ScoreTP_M4_lm, partial=FALSE)

##Save model results
Tol_ScoreTP_M4_lm.res<-data.frame(anova(Tol_ScoreTP_M4_lm))
Tol_ScoreTP_M4_lm.res$Predictor<-rownames(Tol_ScoreTP_M4_lm.res)
Tol_ScoreTP_M4_lm.res$EtaSq<-c(eta_squared(Tol_ScoreTP_M4_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreTP_M4_lm.res$Response<-rep("Color_TP", nrow(Tol_ScoreTP_M4_lm.res))
Tol_ScoreTP_M4_lm.res$TimeP<-rep("M4", nrow(Tol_ScoreTP_M4_lm.res))
Tol_ScoreTP_M4_lm.res<-Tol_ScoreTP_M4_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```

Significant effect of Genotype. Still checking Site*Genotype for comparability across Timepoints.



#### Pairwise
```{r}
#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreTP_M4_lm, pairwise~Genotype | Site)

#Sites within Genotypes
emmeans(Tol_ScoreTP_M4_lm, pairwise~Site | Genotype)

##Save p-values

#Genotypes within Sites
Tol_ScoreTP_M4_lm.geno<-data.frame(emmeans(Tol_ScoreTP_M4_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreTP_M4_lm.geno<-Tol_ScoreTP_M4_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_M4_lm.geno$group1<-paste(Tol_ScoreTP_M4_lm.geno$Site, Tol_ScoreTP_M4_lm.geno$group1, sep="_")
Tol_ScoreTP_M4_lm.geno$group2<-paste(Tol_ScoreTP_M4_lm.geno$Site, Tol_ScoreTP_M4_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreTP_M4_lm.site<-data.frame(emmeans(Tol_ScoreTP_M4_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreTP_M4_lm.site<-Tol_ScoreTP_M4_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_M4_lm.site$group1<-paste(Tol_ScoreTP_M4_lm.site$group1, Tol_ScoreTP_M4_lm.site$Genotype, sep="_")
Tol_ScoreTP_M4_lm.site$group2<-paste(Tol_ScoreTP_M4_lm.site$group2, Tol_ScoreTP_M4_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreTP_M4_lm.p<-rbind(Tol_ScoreTP_M4_lm.geno[,c(1:2,4:8)], Tol_ScoreTP_M4_lm.site[,c(1:2,4:8)])
Tol_ScoreTP_M4_lm.p<-Tol_ScoreTP_M4_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreTP_M4_lm.p$Sig<-ifelse(Tol_ScoreTP_M4_lm.p$p<0.001, "***", ifelse(Tol_ScoreTP_M4_lm.p$p<0.01, "**", ifelse(Tol_ScoreTP_M4_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreTP_M4_lm.p$Response<-rep("Color_TP", nrow(Tol_ScoreTP_M4_lm.p))
Tol_ScoreTP_M4_lm.p$TimeP<-rep("M4", nrow(Tol_ScoreTP_M4_lm.p))

```


### Plot Retention by Site and Genotype
```{r}
##Summary statistics by Site and Genotype
Tol_ScoreTP_M4_SG<-summarySE(TolData_M4, measurevar="Score_TP.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreTP_M4_SG.plot<-ggplot(Tol_ScoreTP_M4_SG, aes(x=Site.Geno, y=Score_TP.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_TP.prop-se, ymax=Score_TP.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention by Timepoint")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o)+ 
  stat_pvalue_manual(data=Tol_ScoreTP_M4_lm.p,  y.position=1.1, step.increase=0.4, label="Sig", hide.ns=TRUE); Tol_ScoreTP_M4_SG.plot

```


## Color Score by Timepoint Timepoint M8

### Run Model
```{r}
##Check normality
hist(TolData_M8$Score_TP.prop)
shapiro.test(TolData_M8$Score_TP.prop)
#Not Normal

##Try square transformation
hist((TolData_M8$Score_TP.prop)^2)
shapiro.test((TolData_M8$Score_TP.prop)^2)
#Not Normal

##Try cubed transformation
hist((TolData_M8$Score_TP.prop)^3)
shapiro.test((TolData_M8$Score_TP.prop)^3)
#Not Normal

##Model as a function of Site and Genotype
##Model with no transformation and check residuals
Tol_ScoreTP_M8_lm<-lm(Score_TP.prop~Site+Genotype+Site:Genotype, data=TolData_M8)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreTP_M8_lm)))

#Q-Q plot
qqnorm(resid(Tol_ScoreTP_M8_lm)); qqline(resid(Tol_ScoreTP_M8_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreTP_M8_lm), resid(Tol_ScoreTP_M8_lm))

```


### Model Results

#### Overall
```{r}
#Model Results
summary(Tol_ScoreTP_M8_lm)
anova(Tol_ScoreTP_M8_lm)

#Effect Size of Predictors
eta_squared(Tol_ScoreTP_M8_lm, partial=FALSE)

##Save model results
Tol_ScoreTP_M8_lm.res<-data.frame(anova(Tol_ScoreTP_M8_lm))
Tol_ScoreTP_M8_lm.res$Predictor<-rownames(Tol_ScoreTP_M8_lm.res)
Tol_ScoreTP_M8_lm.res$EtaSq<-c(eta_squared(Tol_ScoreTP_M8_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreTP_M8_lm.res$Response<-rep("Color_TP", nrow(Tol_ScoreTP_M8_lm.res))
Tol_ScoreTP_M8_lm.res$TimeP<-rep("M8", nrow(Tol_ScoreTP_M8_lm.res))
Tol_ScoreTP_M8_lm.res<-Tol_ScoreTP_M8_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```

No significant effects of Site or Genotype. Still checking Site*Genotype for comparability across Timepoints.


#### Pairwise
```{r}
#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreTP_M8_lm, pairwise~Genotype | Site)

#Sites within Genotypes
emmeans(Tol_ScoreTP_M8_lm, pairwise~Site | Genotype)

##Save p-values

#Genotypes within Sites
Tol_ScoreTP_M8_lm.geno<-data.frame(emmeans(Tol_ScoreTP_M8_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreTP_M8_lm.geno<-Tol_ScoreTP_M8_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_M8_lm.geno$group1<-paste(Tol_ScoreTP_M8_lm.geno$Site, Tol_ScoreTP_M8_lm.geno$group1, sep="_")
Tol_ScoreTP_M8_lm.geno$group2<-paste(Tol_ScoreTP_M8_lm.geno$Site, Tol_ScoreTP_M8_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreTP_M8_lm.site<-data.frame(emmeans(Tol_ScoreTP_M8_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreTP_M8_lm.site<-Tol_ScoreTP_M8_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_M8_lm.site$group1<-paste(Tol_ScoreTP_M8_lm.site$group1, Tol_ScoreTP_M8_lm.site$Genotype, sep="_")
Tol_ScoreTP_M8_lm.site$group2<-paste(Tol_ScoreTP_M8_lm.site$group2, Tol_ScoreTP_M8_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreTP_M8_lm.p<-rbind(Tol_ScoreTP_M8_lm.geno[,c(1:2,4:8)], Tol_ScoreTP_M8_lm.site[,c(1:2,4:8)])
Tol_ScoreTP_M8_lm.p<-Tol_ScoreTP_M8_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreTP_M8_lm.p$Sig<-ifelse(Tol_ScoreTP_M8_lm.p$p<0.001, "***", ifelse(Tol_ScoreTP_M8_lm.p$p<0.01, "**", ifelse(Tol_ScoreTP_M8_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreTP_M8_lm.p$Response<-rep("Color_TP", nrow(Tol_ScoreTP_M8_lm.p))
Tol_ScoreTP_M8_lm.p$TimeP<-rep("M8", nrow(Tol_ScoreTP_M8_lm.p))

```


### Plot Retention by Site and Genotype
```{r}
##Summary statistics by Site and Genotype
Tol_ScoreTP_M8_SG<-summarySE(TolData_M8, measurevar="Score_TP.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreTP_M8_SG.plot<-ggplot(Tol_ScoreTP_M8_SG, aes(x=Site.Geno, y=Score_TP.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_TP.prop-se, ymax=Score_TP.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention by Timepoint")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o); Tol_ScoreTP_M8_SG.plot

#+ stat_pvalue_manual(data=Tol_ScoreTP_M8_lm.p,  y.position=0.65, step.increase=0.25, label="Sig", hide.ns=TRUE) #No significant differences
```


## Color Score by Timepoint Timepoint M12

### Run Model
```{r}
##Check normality
hist(TolData_M12$Score_TP.prop)
shapiro.test(TolData_M12$Score_TP.prop)
#Normal

##Model as a function of Site and Genotype
Tol_ScoreTP_M12_lm<-lm(Score_TP.prop~Site+Genotype+Site:Genotype, data=TolData_M12)

```


#### Check Residuals
```{r}
##Check Normality of Residuals
#Distribution 
plot(density(resid(Tol_ScoreTP_M12_lm)))

#Q-Q plot
qqnorm(resid(Tol_ScoreTP_M12_lm)); qqline(resid(Tol_ScoreTP_M12_lm))

##Check Variance of Residuals across Fitted Values
plot(fitted(Tol_ScoreTP_M12_lm), resid(Tol_ScoreTP_M12_lm))

```


### Model Results

#### Overall
```{r}
#Model Results
summary(Tol_ScoreTP_M12_lm)
anova(Tol_ScoreTP_M12_lm)

#Effect Size of Predictors
eta_squared(Tol_ScoreTP_M12_lm, partial=FALSE)

##Save model results
Tol_ScoreTP_M12_lm.res<-data.frame(anova(Tol_ScoreTP_M12_lm))
Tol_ScoreTP_M12_lm.res$Predictor<-rownames(Tol_ScoreTP_M12_lm.res)
Tol_ScoreTP_M12_lm.res$EtaSq<-c(eta_squared(Tol_ScoreTP_M12_lm, partial=FALSE)$Eta2, NA)
Tol_ScoreTP_M12_lm.res$Response<-rep("Color_TP", nrow(Tol_ScoreTP_M12_lm.res))
Tol_ScoreTP_M12_lm.res$TimeP<-rep("M12", nrow(Tol_ScoreTP_M12_lm.res))
Tol_ScoreTP_M12_lm.res<-Tol_ScoreTP_M12_lm.res %>% dplyr::rename( p.value = "Pr..F.", DF= "Df")

```

No significant effects of Site or Genotype. Still checking Site*Genotype for comparability across Timepoints.


#### Pairwise
```{r}
#Pairwise comparisons across:

#Genotypes within Sites
emmeans(Tol_ScoreTP_M12_lm, pairwise~Genotype | Site)

#Sites within Genotypes
emmeans(Tol_ScoreTP_M12_lm, pairwise~Site | Genotype)

##Save p-values

#Genotypes within Sites
Tol_ScoreTP_M12_lm.geno<-data.frame(emmeans(Tol_ScoreTP_M12_lm, pairwise~Genotype | Site)$contrasts)
Tol_ScoreTP_M12_lm.geno<-Tol_ScoreTP_M12_lm.geno %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_M12_lm.geno$group1<-paste(Tol_ScoreTP_M12_lm.geno$Site, Tol_ScoreTP_M12_lm.geno$group1, sep="_")
Tol_ScoreTP_M12_lm.geno$group2<-paste(Tol_ScoreTP_M12_lm.geno$Site, Tol_ScoreTP_M12_lm.geno$group2, sep="_")

#Sites within Genotypes
Tol_ScoreTP_M12_lm.site<-data.frame(emmeans(Tol_ScoreTP_M12_lm, pairwise~Site | Genotype)$contrasts)
Tol_ScoreTP_M12_lm.site<-Tol_ScoreTP_M12_lm.site %>% separate(col=contrast, into=c("group1", "group2"), sep=" - ", remove=TRUE)
Tol_ScoreTP_M12_lm.site$group1<-paste(Tol_ScoreTP_M12_lm.site$group1, Tol_ScoreTP_M12_lm.site$Genotype, sep="_")
Tol_ScoreTP_M12_lm.site$group2<-paste(Tol_ScoreTP_M12_lm.site$group2, Tol_ScoreTP_M12_lm.site$Genotype, sep="_")

#Full list of p-values
Tol_ScoreTP_M12_lm.p<-rbind(Tol_ScoreTP_M12_lm.geno[,c(1:2,4:8)], Tol_ScoreTP_M12_lm.site[,c(1:2,4:8)])
Tol_ScoreTP_M12_lm.p<-Tol_ScoreTP_M12_lm.p %>% dplyr::rename( p = p.value)

#Add Significance Levels
Tol_ScoreTP_M12_lm.p$Sig<-ifelse(Tol_ScoreTP_M12_lm.p$p<0.001, "***", ifelse(Tol_ScoreTP_M12_lm.p$p<0.01, "**", ifelse(Tol_ScoreTP_M12_lm.p$p<0.05, "*", NA)))

#Specify Response and Timepoint
Tol_ScoreTP_M12_lm.p$Response<-rep("Color_TP", nrow(Tol_ScoreTP_M12_lm.p))
Tol_ScoreTP_M12_lm.p$TimeP<-rep("M12", nrow(Tol_ScoreTP_M12_lm.p))

```


### Plot Retention by Site and Genotype
```{r}
##Summary statistics by Site and Genotype
Tol_ScoreTP_M12_SG<-summarySE(TolData_M12, measurevar="Score_TP.prop", groupvars=c("Site.Geno", "Site", "Genotype"), na.rm=TRUE)

##Plot Average Retention across Treatments
Tol_ScoreTP_M12_SG.plot<-ggplot(Tol_ScoreTP_M12_SG, aes(x=Site.Geno, y=Score_TP.prop, colour=Genotype)) + 
  geom_errorbar(aes(ymin=Score_TP.prop-se, ymax=Score_TP.prop+se), width=cap.sz, linewidth=bar.sz)+
  geom_point(size=point.sz)+ 
   ggtitle("Color Retention by Timepoint")+
  theme_classic()+
  theme( axis.title.x = element_text(size = axis.title.sz), 
         axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"),
        legend.position="bottom", 
        legend.direction="horizontal",
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="Proportion Retained")+
  ylim(0, 1.5)+ 
  scale_x_discrete(labels=c("","Klein","","","Something Special",""))+
  scale_color_manual(values = Geno.colors.o); Tol_ScoreTP_M12_SG.plot

#+ stat_pvalue_manual(data=Tol_ScoreTP_M12_lm.p,  y.position=0.8, step.increase=0.15, label="Sig", hide.ns=TRUE) #No significant differences

```


# Contrasts Across Metrics

### Combine Contrast Results
Creating a heatmap to compare the direction and significance of pairwise comparisons across Tolerance metrics. Positive estimates indicate that Group 1 > Group 2. P values of less than 0.05 are considered significant.   
```{r}
##Combine Results
Tol_contrasts<-rbind(Tol_Chl_W1_lm.p, Tol_Chl_W2_lm.p, Tol_Chl_M1_lm.p, 
                     Tol_Chl_M4_lm.p, Tol_Chl_M8_lm.p, Tol_Chl_M12_lm.p,
                     Tol_Sym_W2_lm.p, Tol_Sym_M1_lm.p, 
                     Tol_Sym_M4_lm.p, Tol_Sym_M8_lm.p, Tol_Sym_M12_lm.p,
                     Tol_ScoreF_W1_lm.p, Tol_ScoreF_W2_lm.p,
                     Tol_ScoreF_M1_lm.p, Tol_ScoreF_M4_lm.p, 
                     Tol_ScoreF_M8_lm.p, Tol_ScoreF_M12_lm.p,
                     Tol_ScoreTP_W1_lm.p, Tol_ScoreTP_W2_lm.p,
                     Tol_ScoreTP_M1_lm.p, Tol_ScoreTP_M4_lm.p, 
                     Tol_ScoreTP_M8_lm.p, Tol_ScoreTP_M12_lm.p)

##Create Contrast Column
Tol_contrasts$Contrast<-paste(Tol_contrasts$group1, Tol_contrasts$group2, sep=" v. ")

```


### Standardize Response Scale
Convert Estimate to the Response scale (instead of log +1 scale) for models where the response was log transformed
```{r}
Tol_contrasts$Estimate<-Tol_contrasts$estimate

#Chl W1
Tol_contrasts$Estimate[which(Tol_contrasts$Response=="Chlorophyll" & Tol_contrasts$TimeP=="W1")]<-c(exp(Tol_contrasts$estimate[which(Tol_contrasts$Response=="Chlorophyll" & Tol_contrasts$TimeP=="W1")])-1)

#Chl M4
Tol_contrasts$Estimate[which(Tol_contrasts$Response=="Chlorophyll" & Tol_contrasts$TimeP=="M4")]<-c(exp(Tol_contrasts$estimate[which(Tol_contrasts$Response=="Chlorophyll" & Tol_contrasts$TimeP=="M4")])-1)

#Chl M12
Tol_contrasts$Estimate[which(Tol_contrasts$Response=="Chlorophyll" & Tol_contrasts$TimeP=="M12")]<-c(exp(Tol_contrasts$estimate[which(Tol_contrasts$Response=="Chlorophyll" & Tol_contrasts$TimeP=="M12")])-1)

#Sym M1
Tol_contrasts$Estimate[which(Tol_contrasts$Response=="Symbionts" & Tol_contrasts$TimeP=="M1")]<-c(exp(Tol_contrasts$estimate[which(Tol_contrasts$Response=="Symbionts" & Tol_contrasts$TimeP=="M1")])-1)


```


## Contrast Heatmaps

### Week 1
```{r}
Tol_Pairs_W1.plot<-ggplot(data=Tol_contrasts[which(Tol_contrasts$TimeP=="W1"),], aes(x=Response, y=Contrast, fill=Estimate))+
  geom_tile()+
  geom_text(aes(label=Sig), size=sig.sz)+
  scale_fill_gradient2(low="#BA1E02FF", mid="white", high="#3BA0FDFF")+
  theme_bw()+
  ggtitle("Pairwise Contrasts Week 1")+
  theme(axis.text.x=element_text(size=axis.title.sz, colour="black", angle=45, hjust=1),
        axis.text.y=element_text(size=axis.txt.sz-1, colour="black"),
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="", fill="Estimate");Tol_Pairs_W1.plot
```


### Week 2
```{r}
Tol_Pairs_W2.plot<-ggplot(data=Tol_contrasts[which(Tol_contrasts$TimeP=="W2"),], aes(x=Response, y=Contrast, fill=Estimate))+
  geom_tile()+
  geom_text(aes(label=Sig), size=sig.sz)+
  scale_fill_gradient2(low="#BA1E02FF", mid="white", high="#3BA0FDFF")+
  theme_bw()+
  ggtitle("Pairwise Contrasts Week 2")+
  theme(axis.text.x=element_text(size=axis.title.sz, colour="black", angle=45, hjust=1),
        axis.text.y=element_text(size=axis.txt.sz-1, colour="black"),
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="", fill="Estimate");Tol_Pairs_W2.plot
```


### Month 1
```{r}
Tol_Pairs_M1.plot<-ggplot(data=Tol_contrasts[which(Tol_contrasts$TimeP=="M1"),], aes(x=Response, y=Contrast, fill=Estimate))+
  geom_tile()+
  geom_text(aes(label=Sig), size=sig.sz)+
  scale_fill_gradient2(low="#BA1E02FF", mid="white", high="#3BA0FDFF")+
  theme_bw()+
  ggtitle("Pairwise Contrasts Month 1")+
  theme(axis.text.x=element_text(size=axis.title.sz, colour="black", angle=45, hjust=1),
        axis.text.y=element_text(size=axis.txt.sz-1, colour="black"),
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="", fill="Estimate");Tol_Pairs_M1.plot
```


### Month 4
```{r}
Tol_Pairs_M4.plot<-ggplot(data=Tol_contrasts[which(Tol_contrasts$TimeP=="M4"),], aes(x=Response, y=Contrast, fill=Estimate))+
  geom_tile()+
  geom_text(aes(label=Sig), size=sig.sz)+
  scale_fill_gradient2(low="#BA1E02FF", mid="white", high="#3BA0FDFF")+
  theme_bw()+
  ggtitle("Pairwise Contrasts Month 4")+
  theme(axis.text.x=element_text(size=axis.title.sz, colour="black", angle=45, hjust=1),
        axis.text.y=element_text(size=axis.txt.sz-1, colour="black"),
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="", fill="Estimate");Tol_Pairs_M4.plot
```


### Month 8
```{r}
Tol_Pairs_M8.plot<-ggplot(data=Tol_contrasts[which(Tol_contrasts$TimeP=="M8"),], aes(x=Response, y=Contrast, fill=Estimate))+
  geom_tile()+
  geom_text(aes(label=Sig), size=sig.sz)+
  scale_fill_gradient2(low="#BA1E02FF", mid="white", high="#3BA0FDFF")+
  theme_bw()+
  ggtitle("Pairwise Contrasts Month 8")+
  theme(axis.text.x=element_text(size=axis.title.sz, colour="black", angle=45, hjust=1),
        axis.text.y=element_text(size=axis.txt.sz-1, colour="black"),
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="", fill="Estimate");Tol_Pairs_M8.plot
```


### Month 12
```{r}
Tol_Pairs_M12.plot<-ggplot(data=Tol_contrasts[which(Tol_contrasts$TimeP=="M12"),], aes(x=Response, y=Contrast, fill=Estimate))+
  geom_tile()+
  geom_text(aes(label=Sig), size=sig.sz)+
  scale_fill_gradient2(low="#BA1E02FF", mid="white", high="#3BA0FDFF")+
  theme_bw()+
  ggtitle("Pairwise Contrasts Month 12")+
  theme(axis.text.x=element_text(size=axis.title.sz, colour="black", angle=45, hjust=1),
        axis.text.y=element_text(size=axis.txt.sz-1, colour="black"),
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="", fill="Estimate");Tol_Pairs_M12.plot
```


### Significant Differences

Subsetting Contrasts to only include contrasts with significant differences in at least one of the thermal tolerance metrics (Chlorophyll or Symbiont or Color retention)
```{r}
##Add Set column with Contrast and Timepoint
Tol_contrasts$Set<-paste(Tol_contrasts$TimeP, Tol_contrasts$Contrast, sep=" ")

##Remove rows with non-significant p-values
Tol_contrasts_sig<-subset(Tol_contrasts, p < 0.05)

##Keep Sets where at least one metric shows a significant difference
Tol_Sets_sig<-data.frame(Set=c(unique(Tol_contrasts_sig$Set)))
Tol_contrasts_sig_Set<-merge(Tol_Sets_sig, Tol_contrasts, all.x=TRUE, all.y=FALSE)

##Re-order by Timepoint
Tol_contrasts_sig_Set$TimeP<-factor(Tol_contrasts_sig_Set$TimeP, levels=c("W1", "W2", "M1", "M4", "M8", "M12"), ordered=TRUE)
Tol_contrasts_sig_Set<- Tol_contrasts_sig_Set %>% arrange(desc(as.numeric(TimeP)))

Tol_contrasts_sig_Set$Set<-factor(Tol_contrasts_sig_Set$Set, levels=c(unique(Tol_contrasts_sig_Set$Set)), ordered=TRUE)
```


```{r}
Tol_Pairs_sig.plot<-ggplot(data=Tol_contrasts_sig_Set, aes(x=Response, y=Set, fill=Estimate))+
  geom_tile()+
  geom_text(aes(label=Sig), size=sig.sz)+
  scale_fill_gradient2(low="#BA1E02FF", mid="white", high="#3BA0FDFF")+
  theme_bw()+
  ggtitle("Significant Pairwise Contrasts")+
  theme(axis.text.x=element_text(size=axis.title.sz, colour="black", angle=45, hjust=1),
        axis.text.y=element_text(size=axis.txt.sz-1, colour="black"),
        legend.text=element_text(size=leg.txt.sz),
        legend.title=element_text(size=leg.title.sz), 
        plot.title = element_text(size = plot.title.sz, colour="black", hjust = 0.5))+
  labs(x="", y="", fill="Estimate");Tol_Pairs_sig.plot
```


# Figures

### Week 1
```{r}
##Create Panel
Tolerance_W1_fig<-plot_grid(Tol_Pairs_W1.plot, Tol_Chl_W1_SG.plot,
                        Tol_ScoreF_W1_SG.plot, Tol_ScoreTP_W1_SG.plot, 
                        nrow=1, ncol=4, 
                        rel_widths=c(1, 0.75, 0.75, 0.75), rel_heights=1, 
                        labels=c("A", "B", "C", "D"), 
                        label_size=panel.lab.sz, 
                        label_fontface = "bold")

##Save Figure
ggsave(filename="Figures/Tolerance_W1.png", plot=Tolerance_W1_fig, dpi=300, width=20, height=6, units="in")

```


### Week 2
```{r}
##Create Panel
Tolerance_W2_fig<-plot_grid(Tol_Pairs_W2.plot, 
                        Tol_Chl_W2_SG.plot, Tol_ScoreF_W2_SG.plot,
                        Tol_ScoreTP_W2_SG.plot, Tol_Sym_W2_SG.plot, 
                        nrow=1, ncol=5, 
                        rel_widths=c(1, 0.75, 0.75, 0.75, 0.75),
                        rel_heights=1, 
                        labels=c("A", "B", "C", "D", "E"), 
                        label_size=panel.lab.sz, 
                        label_fontface = "bold")

##Save Figure
ggsave(filename="Figures/Tolerance_W2.png", plot=Tolerance_W2_fig, dpi=300, width=25, height=6, units="in")

```


### Month 1
```{r}
##Create Panel
Tolerance_M1_fig<-plot_grid(Tol_Pairs_M1.plot, 
                        Tol_Chl_M1_SG.plot, Tol_ScoreF_M1_SG.plot,
                        Tol_ScoreTP_M1_SG.plot, Tol_Sym_M1_SG.plot, 
                        nrow=1, ncol=5, 
                        rel_widths=c(1, 0.75, 0.75, 0.75, 0.75),
                        rel_heights=1, 
                        labels=c("A", "B", "C", "D", "E"), 
                        label_size=panel.lab.sz, 
                        label_fontface = "bold")

##Save Figure
ggsave(filename="Figures/Tolerance_M1.png", plot=Tolerance_M1_fig, dpi=300, width=25, height=6, units="in")

```


### Month 4
```{r}
##Create Panel
Tolerance_M4_fig<-plot_grid(Tol_Pairs_M4.plot, 
                        Tol_Chl_M4_SG.plot, Tol_ScoreF_M4_SG.plot,
                        Tol_ScoreTP_M4_SG.plot, Tol_Sym_M4_SG.plot, 
                        nrow=1, ncol=5, 
                        rel_widths=c(1, 0.75, 0.75, 0.75, 0.75),
                        rel_heights=1, 
                        labels=c("A", "B", "C", "D", "E"), 
                        label_size=panel.lab.sz, 
                        label_fontface = "bold")

##Save Figure
ggsave(filename="Figures/Tolerance_M4.png", plot=Tolerance_M4_fig, dpi=300, width=25, height=6, units="in")

```


### Month 8
```{r}
##Create Panel
Tolerance_M8_fig<-plot_grid(Tol_Pairs_M8.plot, 
                        Tol_Chl_M8_SG.plot, Tol_ScoreF_M8_SG.plot,
                        Tol_ScoreTP_M8_SG.plot, Tol_Sym_M8_SG.plot, 
                        nrow=1, ncol=5, 
                        rel_widths=c(1, 0.75, 0.75, 0.75, 0.75),
                        rel_heights=1, 
                        labels=c("A", "B", "C", "D", "E"), 
                        label_size=panel.lab.sz, 
                        label_fontface = "bold")

##Save Figure
ggsave(filename="Figures/Tolerance_M8.png", plot=Tolerance_M8_fig, dpi=300, width=25, height=6, units="in")

```


### Month 12
```{r}
##Create Panel
Tolerance_M12_fig<-plot_grid(Tol_Pairs_M12.plot, 
                        Tol_Chl_M12_SG.plot, Tol_ScoreF_M12_SG.plot,
                        Tol_ScoreTP_M12_SG.plot, Tol_Sym_M12_SG.plot, 
                        nrow=1, ncol=5, 
                        rel_widths=c(1, 0.75, 0.75, 0.75, 0.75),
                        rel_heights=1, 
                        labels=c("A", "B", "C", "D", "E"), 
                        label_size=panel.lab.sz, 
                        label_fontface = "bold")

##Save Figure
ggsave(filename="Figures/Tolerance_M12.png", plot=Tolerance_M12_fig, dpi=300, width=25, height=6, units="in")

```


### Significant Contrasts Heatmap
```{r}
##Save Figure
ggsave(filename="Figures/Tolerance_Heatmap.png", plot=Tol_Pairs_sig.plot, dpi=300, width=8, height=12, units="in")

```


# Tables

### Linear Model Results
```{r}
##Combine Results Tables
TableS2_Tol.lm.res<-rbind(Tol_Chl_W1_lm.res, Tol_Chl_W2_lm.res, 
                          Tol_Chl_M1_lm.res, Tol_Chl_M4_lm.res,
                          Tol_Chl_M8_lm.res, Tol_Chl_M12_lm.res,
                          Tol_Sym_W2_lm.res, 
                          Tol_Sym_M1_lm.res, Tol_Sym_M4_lm.res,
                          Tol_Sym_M8_lm.res, Tol_Sym_M12_lm.res,
                          Tol_ScoreF_W1_lm.res, Tol_ScoreF_W2_lm.res, 
                          Tol_ScoreF_M1_lm.res, Tol_ScoreF_M4_lm.res,
                          Tol_ScoreF_M8_lm.res, Tol_ScoreF_M12_lm.res,
                          Tol_ScoreTP_W1_lm.res, Tol_ScoreTP_W2_lm.res, 
                          Tol_ScoreTP_M1_lm.res, Tol_ScoreTP_M4_lm.res,
                          Tol_ScoreTP_M8_lm.res, Tol_ScoreTP_M12_lm.res)

##Organize
names(TableS2_Tol.lm.res)
TableS2_Tol.lm.res<-TableS2_Tol.lm.res[,c("TimeP", "Response", "Predictor", "DF", "Sum.Sq", "Mean.Sq", "F.value", "p.value", "EtaSq")]

#Round to 4 digits
TableS2_Tol.lm.res$Sum.Sq<-round(TableS2_Tol.lm.res$Sum.Sq, 4)
TableS2_Tol.lm.res$Mean.Sq<-round(TableS2_Tol.lm.res$Mean.Sq, 4)
TableS2_Tol.lm.res$F.value<-round(TableS2_Tol.lm.res$F.value, 4)
TableS2_Tol.lm.res$p.value<-round(TableS2_Tol.lm.res$p.value, 4)
TableS2_Tol.lm.res$EtaSq<-round(TableS2_Tol.lm.res$EtaSq, 4)

##Write Out Table
write.csv(TableS2_Tol.lm.res, "Tables/TableS2_Tolerance_LM_Results.csv", row.names=FALSE)

```


### Pairwise Comparison Results
```{r}
##Pairwise Results Table
TableS3_Tol.pairwise<-Tol_contrasts

##Organize
names(TableS3_Tol.pairwise)
TableS3_Tol.pairwise<-TableS3_Tol.pairwise[,c("TimeP", "Response", "Contrast", "Estimate", "SE", "df", "t.ratio", "p")]
TableS3_Tol.pairwise<-TableS3_Tol.pairwise %>% dplyr::rename( DF = df) %>% dplyr::rename( p.value = p)

#Round to 4 digits
TableS3_Tol.pairwise$Estimate<-round(TableS3_Tol.pairwise$Estimate, 4)
TableS3_Tol.pairwise$SE<-round(TableS3_Tol.pairwise$SE, 4)
TableS3_Tol.pairwise$t.ratio<-round(TableS3_Tol.pairwise$t.ratio, 4)
TableS3_Tol.pairwise$p.value<-round(TableS3_Tol.pairwise$p.value, 4)

##Write Out Table
write.csv(TableS3_Tol.pairwise, "Tables/TableS3_Tolerance_Pairwise_Results.csv", row.names=FALSE)
```

